Tribute Hall – Perhaps not the exact one (but sure why not) but we do need a place for weapon testing that isn’t pinging those little floaties in the crafting chamber. We lost a lot of things on Leviathan but that was a really important one.

Bungie Wants To Hear The Giant ‘Destiny 2’ Changes You Want To See, Here Are Mine

Destiny 2: The Final Shape is almost here. Here’s how you should prep for launch, including saving bounties and working through content that’s about to go away

How to prepare for Destiny 2: The Final Shape - Polygon

Destiny 2 just rolled out what I would consider three of the biggest changes in its history, no exaggeration, two of which specifically I believe will make the game in...

‘Destiny 2’ Just Made Three Of Its Biggest Changes In Series History

Destiny 2 sunsetting has been removed and with the release of Destiny 2 The Final Shape, any weapons will be available for use. This includes any old Destiny 2 weapons that had been held back for years. We discuss this change on IGN&#x27;s Fireteam Chat this week as well as the Destiny 2 FInal Shape ...

Destiny 2 Sunsetting Removed & Huge Power Changes - Fireteam Chat - IGN

Guns from before the Beyond Light expansion that have Power limits will again be useful thanks to a new Fireteam Power system, but if you deleted your favorites, you&#x27;re not out of luck.

Destiny 2 Finally Undoes "Gunsetting" With The Final Shape's New Power System - GameSpot

Years ago, Bungie removed the ability to effectively use old weapons. Now, the studio is bringing them all back for Destiny 2: The Final Shape on June 4.

Bungie is reversing its decision to sunset your weapons ahead of Destiny 2: The Final Shape

In a major change coming as part of The Final Shape, legacy gear will be upgradeable to the new power cap again.

Remember all those Destiny 2 guns you deleted after they got sunset? Bungie admits you shouldn't have done that: 'We regret we have no recovery mechanism' | PC Gamer

Bungie is un-sunsetting old gear and radically overhauling how power works in The Final Shape

Destiny 2: How Power Caps & Difficulty Work In The Final Shape

Destiny 2: The Final Shape marks the end of the 10-year-old story of Destiny developed by Bungie. Take a look at one of the new enemy factions coming to Destiny 2: The Final Shape being the Grim, agile flying foes that are sure to bother Guardians alike. Destiny 2: The Final Shape launches ...

Destiny 2: The Final Shape - Official Dread Faction 'The Grim' Highlight Trailer

This week, Destiny 2 rolled out an iteration of Guardian Games, its class-based Olympics that pits Hunters, Titans and Warlocks against each other. There are problems

Destiny 2’s Guardian Games Is Kind Of A Mess, 2024 Edition

Testiny

testiny

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Authors:
(1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China;
(2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China
(3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China;
(4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China;
(5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China
(6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA;
(7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA
(8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Table of Links Abstract 1 Introduction 2 Background 3 Approach and Implementation 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.3 Image Generation and 3.4 Properties Assessment 3.5 Bias Evaluation 4 Evaluation 4.1 Experimental Setup 4.2 RQ1: Effectiveness of BiasPainter 4.3 RQ2 - Validity of Identified Biases 4.4 RQ3 - Bias Mitigation 5 Threats to Validity 6 Related Work 7 Conclusion, Data Availability, and References 6 RELATED WORK 6.1 Testing of AI Software AI software has been adopted in various domains, such as autonomous driving and face recognition. However, AI software is not robust enough and can generate erroneous outputs that lead to fatal accidents [24, 63]. To this end, researchers have proposed a variety of methods to generate adversarial examples or test cases to measure the reliability of AI software [7, 22, 27, 28, 31, 32, 36, 43, 45, 48, 50, 51, 58, 59, 62]. NLP software, such as machine translation software and chatbot, has also been widely used in human life. Similar to AI software, researchers have proposed variance methods to validate the reliability of NLP software on the correctness [18, 19, 40, 44], toxicity [53, 54], fairness [47, 52, 57]. In contrast to the aforementioned AI software, image generation models serve as a new multimodal AI software that emerged recently, posing challenges for researchers to precisely and comprehensively evaluate and test. 6.2 Bias and Fairness in AI Software Bias and fairness have gained significant attention in the SE community from various perspectives, such as bias testing [29] and bias mitigation [20], and for various kinds of software, such as nature language processing models [42], recommendation systems [33], chatbot [47], and autonomous driving car [25]. As one of the most popular AI software, the image generation model is widely used with a sufficient amount of active users. However, to the best of our knowledge, the comprehensive testing framework for the bias and fairness in image generation models remains under investigation. In this paper, we focus on image generation models and propose the first automatic and comprehensive testing framework. 6.3 Bias in Image Generation Models We systematically reviewed papers on testing the biases in image generation models across related research areas, including software engineering, computer vision, natural language processing, and security. [3] is an early work that conducts an empirical study to show the stereotypes learned by text-to-image models. They design different prompts as input and use human annotators to find biased images, without proposing an automatic framework that can trigger the social bias. Inspired by this, [12] proposed an automatic framework to evaluate the bias in image generation models. However, their automatic evaluation method failed to accurately detect the bias according to their human evaluation. Also, the generated images are highly biased toward white people so their framework cannot analyze the bias in other groups. More recently, [49] study the gender stereotype in occupations, but the scope of which is limited. Different from the aforementioned works, BiasPainter is the first framework that can automatically, comprehensively, and accurately reveal the social bias in image generation models. This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China; (2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China (3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China; (4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China; (5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China (6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA; (7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA (8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Authors: Authors: (1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China; (2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China (3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China; (4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China; (5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China (6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA; (7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA (8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Background 2 Background 3 Approach and Implementation 3 Approach and Implementation 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.3 Image Generation and 3.4 Properties Assessment 3.3 Image Generation and 3.4 Properties Assessment 3.5 Bias Evaluation 3.5 Bias Evaluation 4 Evaluation 4 Evaluation 4.1 Experimental Setup 4.1 Experimental Setup 4.2 RQ1: Effectiveness of BiasPainter 4.2 RQ1: Effectiveness of BiasPainter 4.3 RQ2 - Validity of Identified Biases 4.3 RQ2 - Validity of Identified Biases 4.4 RQ3 - Bias Mitigation 4.4 RQ3 - Bias Mitigation 5 Threats to Validity 5 Threats to Validity 6 Related Work 6 Related Work 7 Conclusion, Data Availability, and References 7 Conclusion, Data Availability, and References 6 RELATED WORK 6.1 Testing of AI Software AI software has been adopted in various domains, such as autonomous driving and face recognition. However, AI software is not robust enough and can generate erroneous outputs that lead to fatal accidents [24, 63]. To this end, researchers have proposed a variety of methods to generate adversarial examples or test cases to measure the reliability of AI software [7, 22, 27, 28, 31, 32, 36, 43, 45, 48, 50, 51, 58, 59, 62]. NLP software, such as machine translation software and chatbot, has also been widely used in human life. Similar to AI software, researchers have proposed variance methods to validate the reliability of NLP software on the correctness [18, 19, 40, 44], toxicity [53, 54], fairness [47, 52, 57]. In contrast to the aforementioned AI software, image generation models serve as a new multimodal AI software that emerged recently, posing challenges for researchers to precisely and comprehensively evaluate and test. 6.2 Bias and Fairness in AI Software Bias and fairness have gained significant attention in the SE community from various perspectives, such as bias testing [29] and bias mitigation [20], and for various kinds of software, such as nature language processing models [42], recommendation systems [33], chatbot [47], and autonomous driving car [25]. As one of the most popular AI software, the image generation model is widely used with a sufficient amount of active users. However, to the best of our knowledge, the comprehensive testing framework for the bias and fairness in image generation models remains under investigation. In this paper, we focus on image generation models and propose the first automatic and comprehensive testing framework. 6.3 Bias in Image Generation Models We systematically reviewed papers on testing the biases in image generation models across related research areas, including software engineering, computer vision, natural language processing, and security. [3] is an early work that conducts an empirical study to show the stereotypes learned by text-to-image models. They design different prompts as input and use human annotators to find biased images, without proposing an automatic framework that can trigger the social bias. Inspired by this, [12] proposed an automatic framework to evaluate the bias in image generation models. However, their automatic evaluation method failed to accurately detect the bias according to their human evaluation. Also, the generated images are highly biased toward white people so their framework cannot analyze the bias in other groups. More recently, [49] study the gender stereotype in occupations, but the scope of which is limited. Different from the aforementioned works, BiasPainter is the first framework that can automatically, comprehensively, and accurately reveal the social bias in image generation models. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Exploring Bias and Fairness in AI: The Need for Comprehensive Testing Frameworks

Authors:
(1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China;
(2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China
(3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China;
(4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China;
(5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China
(6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA;
(7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA
(8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Table of Links Abstract 1 Introduction 2 Background 3 Approach and Implementation 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.3 Image Generation and 3.4 Properties Assessment 3.5 Bias Evaluation 4 Evaluation 4.1 Experimental Setup 4.2 RQ1: Effectiveness of BiasPainter 4.3 RQ2 - Validity of Identified Biases 4.4 RQ3 - Bias Mitigation 5 Threats to Validity 6 Related Work 7 Conclusion, Data Availability, and References 3.3 Image Generation In this section, we introduce how BiasPainter inputs the seed image and text prompt to image generation models to generate images. As introduced in Section 3.1 and 3.2, the seed image set consists of 54 photos of people, from different combinations of race, age and gender, while the neutral prompt list consists of 228 sentences generated by different prompt words across the 4 topics. Meanwhile, image generation models enable users to provide additional descriptive prompts that can help control the generated style. For each seed image, BiasPainter inputs each prompt from the neutral prompt list every time to generate images. Finally, BiasPainter generates 54 * 228 = 12312 images, which are used to identify the social bias. 3.4 Properties Assessment BiasPainter adopts the (seed image, generated image) pairs to evaluate the social bias. For each seed image and the generated image, BiasPainter first adopts AI techniques to evaluate their properties according to race, gender, and age. Race Assessment. We follow [12] to analyse the race according to the skin color. Researchers presented a division into six groups based on color adjectives: White (Caucasian), Dusky (South Asian), Orange (Austronesian), Yellow (East Asian), Red (Indigenous American), and Black (African) [41]. It is challenging to distinguish the race of a human accurately, considering the number of the race and their mix. To make it simple, BiasPainter uses the significant change of skin tone to identify the changing of the race. For each image, BiasPainter adopts an image processing pipeline to access the skin tone, as illustrated in Figure 4. First, BiasPainter calls Dlib [8] to get a 68-point face landmark and find the area of the face in the picture. The landmark provides the position and the shape of the face in the picture, and also marks the eyes and mouth of the face. Then, BiasPainter adopts a rule-based method to remove the background, eyes and mouth from the face. Finally, BiasPainter calculates the average pixel value of the remaining face. The darker the digital image is, the higher the average pixel value is. The more difference between the average pixel value of the seed image and the generated image, the more possibility that the race is changed by the image generation model. Gender Assessment. While there is a broad spectrum of genders [23], it’s difficult to accurately identify someone’s gender across this broad spectrum based solely on visual cues. Consequently, following previous works [2, 12, 61], we restrict our bias measurement to a binary gender framework and only consider male and female. BiasPainter adopts a commercial face analyses API, named Face++ Cognitive Service [9], to identify the gender information of the human’s picture. Specifically, Face++ Cognitive Service returns a predicted gender to indicate the gender of the people in the picture, which will be adopted by BiasPainter to access the gender. If there is a difference between the gender attribute of the seed image and the generated image, then the image generation model changed the gender in the generated image. Age Assessment. BiasPainter adopts a commercial face analyses API, named Face++ Cognitive Service, to identify the age information of the human’s picture. Specifically, Face++ Cognitive Service returns a predicted age to indicate the ages of the people in the picture, which will be adopted by BiasPainter to access the age. The more differences between the predicted ages of the seed image and the generated image, the more possibility that the age is changed by the image generation model. This paper is available on arxiv under CC0 1.0 DEED license. [8] http://dlib.net/ [9] https://www.faceplusplus.com/ Authors: (1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China; (2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China (3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China; (4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China; (5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China (6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA; (7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA (8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Authors: Authors: (1) Wenxuan Wang, The Chinese University of Hong Kong, Hong Kong, China; (2) Haonan Bai, The Chinese University of Hong Kong, Hong Kong, China (3) Jen-tse Huang, The Chinese University of Hong Kong, Hong Kong, China; (4) Yuxuan Wan, The Chinese University of Hong Kong, Hong Kong, China; (5) Youliang Yuan, The Chinese University of Hong Kong, Shenzhen Shenzhen, China (6) Haoyi Qiu University of California, Los Angeles, Los Angeles, USA; (7) Nanyun Peng, University of California, Los Angeles, Los Angeles, USA (8) Michael Lyu, The Chinese University of Hong Kong, Hong Kong, China. Table of Links Abstract Abstract 1 Introduction 1 Introduction 2 Background 2 Background 3 Approach and Implementation 3 Approach and Implementation 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.1 Seed Image Collection and 3.2 Neutral Prompt List Collection 3.3 Image Generation and 3.4 Properties Assessment 3.3 Image Generation and 3.4 Properties Assessment 3.5 Bias Evaluation 3.5 Bias Evaluation 4 Evaluation 4 Evaluation 4.1 Experimental Setup 4.1 Experimental Setup 4.2 RQ1: Effectiveness of BiasPainter 4.2 RQ1: Effectiveness of BiasPainter 4.3 RQ2 - Validity of Identified Biases 4.3 RQ2 - Validity of Identified Biases 4.4 RQ3 - Bias Mitigation 4.4 RQ3 - Bias Mitigation 5 Threats to Validity 5 Threats to Validity 6 Related Work 6 Related Work 7 Conclusion, Data Availability, and References 7 Conclusion, Data Availability, and References 3.3 Image Generation In this section, we introduce how BiasPainter inputs the seed image and text prompt to image generation models to generate images. As introduced in Section 3.1 and 3.2, the seed image set consists of 54 photos of people, from different combinations of race, age and gender, while the neutral prompt list consists of 228 sentences generated by different prompt words across the 4 topics. Meanwhile, image generation models enable users to provide additional descriptive prompts that can help control the generated style. For each seed image, BiasPainter inputs each prompt from the neutral prompt list every time to generate images. Finally, BiasPainter generates 54 * 228 = 12312 images, which are used to identify the social bias. 3.4 Properties Assessment BiasPainter adopts the (seed image, generated image) pairs to evaluate the social bias. For each seed image and the generated image, BiasPainter first adopts AI techniques to evaluate their properties according to race, gender, and age. Race Assessment. We follow [12] to analyse the race according to the skin color. Researchers presented a division into six groups based on color adjectives: White (Caucasian), Dusky (South Asian), Orange (Austronesian), Yellow (East Asian), Red (Indigenous American), and Black (African) [41]. It is challenging to distinguish the race of a human accurately, considering the number of the race and their mix. To make it simple, BiasPainter uses the significant change of skin tone to identify the changing of the race. For each image, BiasPainter adopts an image processing pipeline to access the skin tone, as illustrated in Figure 4. First, BiasPainter calls Dlib [8] to get a 68-point face landmark and find the area of the face in the picture. The landmark provides the position and the shape of the face in Race Assessment. the picture, and also marks the eyes and mouth of the face. Then, BiasPainter adopts a rule-based method to remove the background, eyes and mouth from the face. Finally, BiasPainter calculates the average pixel value of the remaining face. The darker the digital image is, the higher the average pixel value is. The more difference between the average pixel value of the seed image and the generated image, the more possibility that the race is changed by the image generation model. Gender Assessment. While there is a broad spectrum of genders [23], it’s difficult to accurately identify someone’s gender across this broad spectrum based solely on visual cues. Consequently, following previous works [2, 12, 61], we restrict our bias measurement to a binary gender framework and only consider male and female. BiasPainter adopts a commercial face analyses API, named Face++ Cognitive Service [9], to identify the gender information of the human’s picture. Specifically, Face++ Cognitive Service returns a predicted gender to indicate the gender of the people in the picture, which will be adopted by BiasPainter to access the gender. If there is a difference between the gender attribute of the seed image and the generated image, then the image generation model changed the gender in the generated image. Gender Assessment. Age Assessment. BiasPainter adopts a commercial face analyses API, named Face++ Cognitive Service, to identify the age information of the human’s picture. Specifically, Face++ Cognitive Service returns a predicted age to indicate the ages of the people in the picture, which will be adopted by BiasPainter to access the age. The more differences between the predicted ages of the seed image and the generated image, the more possibility that the age is changed by the image generation model. Age Assessment. This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv [8] http://dlib.net/ [9] https://www.faceplusplus.com/

How AI Models Create and Modify Images for Bias Testing

My Testing Journey With Jasmine and Mocha: What I've Learned

The UX design scene is quickly becoming the newest arena for AI-powered innovation. As an industry all about creating a positive experience for all consumers, UX designers, marketers, and site builders have worked together for years to craft brilliant user experiences manually. However, thanks to the rapid development of machine learning technology, modern-day UX teams can also prioritize speed as well as precision when crafting online experiences. According to research by Forrester, a positive UX design can increase conversions by more than 400%. When integrating AI and machine learning into your strategy, you ensure that every base is covered. In fact, a staggering 62% of UX designers now claim to already use machine learning to increase their productivity, personalize their design and automate tasks such as A/B and accessibility testing. With this in mind, we’re exploring the potential benefits of introducing machine learning to your UX strategy in 2024. What is Machine Learning (ML)? Many of us confuse the term machine learning with artificial intelligence. However, these are both different practices. Machine learning is just one field of artificial intelligence. It focuses on the development of models that can learn from data to enhance predictions and automate decisions. One example of a machine learning model is the famous ChatGPT. As a generative AI tool, ChatGPT's responses have appeared more humanized as it learns more about our language structure from millions of prompts worldwide. The best thing about ML is its ability to improve over time. As the model receives more data, it continues to learn and improve its performance. Here are some of the ways ML could be applied to your UX strategy: Improving user experience using predictive analytics
Customizing the user journey
A/B and accessibility testing
SEO optimization
Detecting potential flaws in your current UX design Now, let’s examine these in more detail and learn how you can seamlessly integrate ML into your current UX strategy. Predictive analytics It’s no secret that ML thrives on new data. With vast amounts of user behavior data to analyze, machine learning models can access your current site interaction and make accurate predictions based on behaviors, user preferences, and gratifications. This is why ML has become the cornerstone of offering the perfect personalized UX design. With ML at hand, you can easily customize content based on each user. Take brands like Spotify and Netflix, for example. They use ML to help adapt their user interfaces. Machine learning helps them create personalized playlists, predict a unique set of shows to watch, and even enhance color schemes to suit individual needs. In fact, according to McKinsey, 71% of consumers expect brands to automatically personalize content to their individual needs in 2024, rendering ML and AI essential in the design process if you’re to see continued engagement on site. A/B testing Next, let’s discuss A/B testing. While this is a staple of UX design practices, it can be a long and tedious process, especially when designers need to move quickly. While ML cannot replace human input, it can take your A/B strategy to another level. It can automatically and autonomously analyze test results before making real-time adjustments to your website in response. Machine learning programs can also analyze user data on the go, such as a user’s session time, location, categories and products viewed, and even an individual page’s bounce rate. Websites managed by ML are always in ‘testing mode,’ meaning that designers can highlight flaws in real-time and adjust without the need to perform regular A/B-style checks. SEO optimisation There are plenty of AI SEO tools on the market, but machine learning takes SEO optimization one step further. With ML tools like ChatGPT on the market, automated content generation is one key reason why ML is reshaping SEO content creation. ML algorithms can generate high-quality content based on in-depth topic modeling and keyword research. This means that your content will be professional as well as highly searchable. Better still, ML algorithms can also help UX designers to generate searchable meta tags and optimised site descriptions. Using data based on user queries, ML can suggest tags and description phrases that are likely to be searched by your target demographic. You can also add an ML-powered competitor analysis to your SEO strategy. By analyzing competitor content, ML models can identify opportunities within the SERPs to place content exactly where it is most likely to be seen. Design accessibility In a study of over a million websites, experts at WebAim found an average of 51.4 accessibility errors per web page. In an era where people of all abilities can access the internet, accessible and inclusive UX design is crucial if you want to appeal to a wide convertible audience. Machine learning has become an essential tool in the creation of accessible online content. ML models can analyze a webpage's content, visual appearance, and CTAs. When programmed to see and think like a user with certain accessibility issues, ML models can immediately highlight areas for site improvement to create the best possible experience for all consumers. A great example of this in action comes from Microsoft’s Seeing AI app. Using computer vision and ML to help identify areas of a website that may be inaccessible to the visually impaired, Seeing AI automatically analyzes the real-time environment and can provide audio descriptions of elements some users are unable to interact with, such as colors and visuals. Detect potential issues with your UX design Machine learning models are not only excellent at gathering data from user-generated content, but also AI-powered content too. ML models may be brilliant content generators, but they can also be used to detect AI writing. If you’re using AI content generators such as ChatGPT, it’s important to make sure that you’re using ML prompts to inspire your own site content rather than simply copying and pasting it. This is because Google itself can detect the use of AI-generated content and, in turn, grant your website a lower authority score. Machine learning models can also help you identify issues with your UX design in real time. By working their way through your site navigation in the same way a human user would, ML models can highlight areas of the consumer journey that may encourage bounce. The benefits of operating in real-time also mean that ML models can detect sudden behavior changes surrounding trends and seasonal browsing patterns, giving you time to adapt your navigation map ahead of the curve. Will AI Replace UX Designers? The rise of AI within the UX industry has significantly reshaped the job market. Machine learning alone has transformed content creation and accessibility testing and helped brands create personalized experiences for their customers. However, could it one day replace UX designers themselves? The short answer is no. While we’re sure Ml will continue to amplify designer capabilities, human UX designers' skills are simply irreplaceable. The UX design scene is quickly becoming the newest arena for AI-powered innovation. As an industry all about creating a positive experience for all consumers, UX designers, marketers, and site builders have worked together for years to craft brilliant user experiences manually. However, thanks to the rapid development of machine learning technology, modern-day UX teams can also prioritize speed as well as precision when crafting online experiences. According to research by Forrester , a positive UX design can increase conversions by more than 400%. When integrating AI and machine learning into your strategy, you ensure that every base is covered. Forrester Forrester In fact, a staggering 62% of UX designers now claim to already use machine learning to increase their productivity, personalize their design and automate tasks such as A/B and accessibility testing. 62% of UX designers 62% of UX designers With this in mind, we’re exploring the potential benefits of introducing machine learning to your UX strategy in 2024. What is Machine Learning (ML)? Many of us confuse the term machine learning with artificial intelligence. However, these are both different practices. Machine learning is just one field of artificial intelligence. It focuses on the development of models that can learn from data to enhance predictions and automate decisions. One example of a machine learning model is the famous ChatGPT. As a generative AI tool, ChatGPT's responses have appeared more humanized as it learns more about our language structure from millions of prompts worldwide. The best thing about ML is its ability to improve over time. As the model receives more data, it continues to learn and improve its performance. Here are some of the ways ML could be applied to your UX strategy : UX strategy UX strategy Improving user experience using predictive analytics Customizing the user journey A/B and accessibility testing SEO optimization Detecting potential flaws in your current UX design Improving user experience using predictive analytics Customizing the user journey A/B and accessibility testing SEO optimization Detecting potential flaws in your current UX design Now, let’s examine these in more detail and learn how you can seamlessly integrate ML into your current UX strategy. Predictive analytics It’s no secret that ML thrives on new data. With vast amounts of user behavior data to analyze, machine learning models can access your current site interaction and make accurate predictions based on behaviors, user preferences, and gratifications. This is why ML has become the cornerstone of offering the perfect personalized UX design. With ML at hand, you can easily customize content based on each user. Take brands like Spotify and Netflix, for example. They use ML to help adapt their user interfaces. Machine learning helps them create personalized playlists, predict a unique set of shows to watch, and even enhance color schemes to suit individual needs. In fact, according to McKinsey, 71% of consumers expect brands to automatically personalize content to their individual needs in 2024, rendering ML and AI essential in the design process if you’re to see continued engagement on site. 71% of consumers 71% of consumers A/B testing Next, let’s discuss A/B testing. While this is a staple of UX design practices, it can be a long and tedious process, especially when designers need to move quickly. While ML cannot replace human input, it can take your A/B strategy to another level. It can automatically and autonomously analyze test results before making real-time adjustments to your website in response. A/B strategy A/B strategy Machine learning programs can also analyze user data on the go, such as a user’s session time, location, categories and products viewed, and even an individual page’s bounce rate. Websites managed by ML are always in ‘testing mode,’ meaning that designers can highlight flaws in real-time and adjust without the need to perform regular A/B-style checks. SEO optimisation There are plenty of AI SEO tools on the market, but machine learning takes SEO optimization one step further. With ML tools like ChatGPT on the market, automated content generation is one key reason why ML is reshaping SEO content creation. ML algorithms can generate high-quality content based on in-depth topic modeling and keyword research. This means that your content will be professional as well as highly searchable. Better still, ML algorithms can also help UX designers to generate searchable meta tags and optimised site descriptions. Using data based on user queries, ML can suggest tags and description phrases that are likely to be searched by your target demographic. You can also add an ML-powered competitor analysis to your SEO strategy. By analyzing competitor content, ML models can identify opportunities within the SERPs to place content exactly where it is most likely to be seen. Design accessibility In a study of over a million websites, experts at WebAim found an average of 51.4 accessibility errors per web page. 51.4 accessibility errors 51.4 accessibility errors In an era where people of all abilities can access the internet, accessible and inclusive UX design is crucial if you want to appeal to a wide convertible audience. Machine learning has become an essential tool in the creation of accessible online content. ML models can analyze a webpage's content, visual appearance, and CTAs. When programmed to see and think like a user with certain accessibility issues, ML models can immediately highlight areas for site improvement to create the best possible experience for all consumers. A great example of this in action comes from Microsoft’s Seeing AI app. Using computer vision and ML to help identify areas of a website that may be inaccessible to the visually impaired, Seeing AI automatically analyzes the real-time environment and can provide audio descriptions of elements some users are unable to interact with, such as colors and visuals. Detect potential issues with your UX design Machine learning models are not only excellent at gathering data from user-generated content, but also AI-powered content too. ML models may be brilliant content generators, but they can also be used to detect AI writing. If you’re using AI content generators such as ChatGPT, it’s important to make sure that you’re using ML prompts to inspire your own site content rather than simply copying and pasting it. This is because Google itself can detect the use of AI-generated content and, in turn, grant your website a lower authority score. ML prompts ML prompts Machine learning models can also help you identify issues with your UX design in real time. By working their way through your site navigation in the same way a human user would, ML models can highlight areas of the consumer journey that may encourage bounce. The benefits of operating in real-time also mean that ML models can detect sudden behavior changes surrounding trends and seasonal browsing patterns, giving you time to adapt your navigation map ahead of the curve. Will AI Replace UX Designers? The rise of AI within the UX industry has significantly reshaped the job market. Machine learning alone has transformed content creation and accessibility testing and helped brands create personalized experiences for their customers. However, could it one day replace UX designers themselves? The short answer is no. could it one day replace could it one day replace While we’re sure Ml will continue to amplify designer capabilities, human UX designers' skills are simply irreplaceable.

Automated A/B Testing: 5 Ways To Use ML To Improve Your UX Design

I feel like there’s always been a love-hate relationship with the concept of testing. Without a doubt, the benefits of testing whatever you are building help avoid customers reporting those same discoveries. That’s the love part of the relationship. The hate part is when project timelines cause testing to become a lower priority … often to the point where it becomes a backlog wishlist item that rarely surfaces in a current sprint. This almost guarantees customers will contact you with unexpected outcomes. As software development lifecycles (SDLCs) have matured, the testing has become easier, allowing developers to create unit tests that fully cover the aspect being validated. The use of ChatGPT or GitHub Co-Pilot has matured to the point where such unit tests can be auto-generated. Continuous integration (CI) tooling solutions have improved to enforce high levels of code coverage before any pull request (PR) can be merged. Doing so allows teams to shift left with their development – forcing problems to be addressed during the development phase and cutting down the cost of features along the way. This approach has worked great for traditional APIs and web frameworks, but testing mobile applications often requires teams to perform manual testing—executing from a published list of steps across as many different device types as can be managed. I wanted to see if I could identify a way mobile development and testing could employ the shift-left concept. What’s Missing in Mobile Testing At a high level, the mobile application space must have the ability to test features and functionality in the same way APIs and web frameworks are testing today. This means getting out of the business of performing tests manually using an inventory of physical devices or emulators. This ideal state of mobile testing would be UI-driven to avoid writing cryptic tests focused on user activity. This approach could expand the tooling to internal consumers or product owners who want to validate their vision as a reality. Just like traditional unit or integration tests, the ability to introduce modules can validate small aspects of the mobile application and be used as building blocks for larger flows. In doing this, teams will be able to stay “dry” (don’t repeat yourself) and avoid duplicate work. Finally, these tests will need to be able to become part of the CI/CD pipeline, despite being driven by a graphical UI. In this ideal state, mobile software engineers can effectively shift left for their mobile development and testing. What is “Shift Left” Anyway? It’s a good idea to clarify what I mean when I say “shift left” for mobile testing. Wikipedia defines shift left for testing as noted below: “Shift-left testing is an approach to software testing and system testing in which testing is performed earlier in the lifecycle (i.e. moved left on the project timeline). It is the first half of the maxim ‘test early and often’. It was coined by Larry Smith in 2001.” Simply embracing shift left allows defects to be identified during the development phase. This is important since the issue can be addressed while the feature is fresh in the mind of the engineer(s) focused on the source. Here are some of the benefits of adopting shift left: Cost reduction to deliver features by finding and fixing issues at development time.
Higher efficiency due to a reduced amount of time required to revisit solutions long after delivery.
Higher quality due to being forced to fully cover and validate during the development phase. Ultimately, the shift left concept will lead to a competitive advantage when solutions reach consumers that are validated and working as expected. About Tosca Last year, I explored the use of Tricentis Testim in my “Improving Customer-Facing App Quality Using Tricentis Testim” article. I was impressed at how easy it was to validate my Magic 8 Ball solution using a GO-based RESTful API and Vue.js web-based client. I wanted to see if Tricentis had a solution that would allow teams to shift left for mobile testing. Turns out, they have a product called Tosca. The Tosca product provides codeless test generation, allowing the creation of small modules that can be reused and automated. These modules can be thought of as Lego blocks that can be connected where needed due to the employment of a standardized contract between them. Tosca takes things a step closer to more traditional development lifecycles by providing the ability to leverage AI to help generate mobile tests for your features. Tosca also leverages the power of the open source Appium project without a heavy learning curve via the Tricentis Mobile Agent. This allows all the power provided in my prior article to be included in the shift left journey with mobile development. As a result, Tosca allows testing of native, hybrid, and web mobile apps across real iPhone, Android, mobile phone, and tablet devices without maintaining and possessing these devices. Just like the Testim product, the Tosca solution provides the ability for tests to be executed as part of CI/CD pipelines, allowing for the enforcement of shift left adoption. You can use Tosca to test on an iOS or Android phone directly, as well as through emulators or simulators available through an IDE like Android Studio. Using Tosca, you can scan your application and have it create test cases: Once Tosca has created the test cases, you can also create test cases of your own. One of the advantages of Tosca is that it allows you to write tests without having to write code. This is made possible through its library of modules that can simulate most every action, including opening a browser or filling out a form. In this example, we’ve used three modules for our Tosca mobile test case. We test: Opening a browser and hitting our app’s endpoint
Selecting a particular type of vehicle
Filling up a form about that particular vehicle Notice that all we had to do is provide sample inputs (in the screenshot, we’re doing so for step 3 highlighted above). Once the test is complete, you’ll receive a diagnostics report on Tosca. Shift left for Mobile Testing By leveraging a product like Tosca, software engineers focused on mobile development can put their teams at a competitive advantage by employing shit left for mobile testing: Mobile features are validated during the development phase, similar to services and web frameworks.
Tests are driven by a UI that can be expanded to internal consumers and product owners to help solidify their vision.
The test suite can be built from a collection of “dry” modules that are structured to fully cover new features and functionality.
Tests can be executed against an exhaustive inventory of mobile devices without owning and maintaining such devices.
Before introducing new features to the primary code branch, the associated PR would have called the UI-generated tests in the same way unit or integration tests are executed in API or web framework CI/CD pipelines. Conclusion My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional: “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” - J. Vester To reach peak productivity, software engineers focused on mobile development need to adopt shift left for mobile testing. However, the ideal choice should consider any associated learning curve and supportability when searching for solutions. The Tosca product adheres to my personal mission statement by allowing teams to reach the shift-left state without additional source code to support and maintain. The product also allows non-engineers to participate in the test development, giving teams an advantage in ensuring designs match expectations. I’ve personally employed the shift left approach for several years and appreciate the experience every time a defect is avoided due to simply following the process. It’s time for mobile development to employ the concept of shift left. Have a really great day! I feel like there’s always been a love-hate relationship with the concept of testing. Without a doubt, the benefits of testing whatever you are building help avoid customers reporting those same discoveries. That’s the love part of the relationship. love The hate part is when project timelines cause testing to become a lower priority … often to the point where it becomes a backlog wishlist item that rarely surfaces in a current sprint. This almost guarantees customers will contact you with unexpected outcomes. hate As software development lifecycles (SDLCs) have matured, the testing has become easier, allowing developers to create unit tests that fully cover the aspect being validated. The use of ChatGPT or GitHub Co-Pilot has matured to the point where such unit tests can be auto-generated. Continuous integration (CI) tooling solutions have improved to enforce high levels of code coverage before any pull request (PR) can be merged. Doing so allows teams to shift left with their development – forcing problems to be addressed during the development phase and cutting down the cost of features along the way. This approach has worked great for traditional APIs and web frameworks, but testing mobile applications often requires teams to perform manual testing—executing from a published list of steps across as many different device types as can be managed. I wanted to see if I could identify a way mobile development and testing could employ the shift-left concept. I wanted to see if I could identify a way mobile development and testing could employ the shift-left concept. What’s Missing in Mobile Testing At a high level, the mobile application space must have the ability to test features and functionality in the same way APIs and web frameworks are testing today. This means getting out of the business of performing tests manually using an inventory of physical devices or emulators. This ideal state of mobile testing would be UI-driven to avoid writing cryptic tests focused on user activity. This approach could expand the tooling to internal consumers or product owners who want to validate their vision as a reality. Just like traditional unit or integration tests, the ability to introduce modules can validate small aspects of the mobile application and be used as building blocks for larger flows. In doing this, teams will be able to stay “dry” (don’t repeat yourself) and avoid duplicate work. Finally, these tests will need to be able to become part of the CI/CD pipeline, despite being driven by a graphical UI. In this ideal state, mobile software engineers can effectively shift left for their mobile development and testing. What is “Shift Left” Anyway? It’s a good idea to clarify what I mean when I say “shift left” for mobile testing. Wikipedia defines shift left for testing as noted below: shift left for testing “Shift-left testing is an approach to software testing and system testing in which testing is performed earlier in the lifecycle (i.e. moved left on the project timeline). It is the first half of the maxim ‘test early and often’. It was coined by Larry Smith in 2001.” “Shift-left testing is an approach to software testing and system testing in which testing is performed earlier in the lifecycle (i.e. moved left on the project timeline). It is the first half of the maxim ‘test early and often’. It was coined by Larry Smith in 2001 .” Larry Smith in 2001 Simply embracing shift left allows defects to be identified during the development phase. This is important since the issue can be addressed while the feature is fresh in the mind of the engineer(s) focused on the source. Here are some of the benefits of adopting shift left: Cost reduction to deliver features by finding and fixing issues at development time. Higher efficiency due to a reduced amount of time required to revisit solutions long after delivery. Higher quality due to being forced to fully cover and validate during the development phase. Cost reduction to deliver features by finding and fixing issues at development time. Higher efficiency due to a reduced amount of time required to revisit solutions long after delivery. Higher quality due to being forced to fully cover and validate during the development phase. Ultimately, the shift left concept will lead to a competitive advantage when solutions reach consumers that are validated and working as expected. About Tosca Last year, I explored the use of Tricentis Testim in my “ Improving Customer-Facing App Quality Using Tricentis Testim ” article. I was impressed at how easy it was to validate my Magic 8 Ball solution using a GO-based RESTful API and Vue.js web-based client. I wanted to see if Tricentis had a solution that would allow teams to shift left for mobile testing. Improving Customer-Facing App Quality Using Tricentis Testim Turns out, they have a product called Tosca . Tosca The Tosca product provides codeless test generation, allowing the creation of small modules that can be reused and automated. These modules can be thought of as Lego blocks that can be connected where needed due to the employment of a standardized contract between them. Tosca takes things a step closer to more traditional development lifecycles by providing the ability to leverage AI to help generate mobile tests for your features. Tosca also leverages the power of the open source Appium project without a heavy learning curve via the Tricentis Mobile Agent. This allows all the power provided in my prior article to be included in the shift left journey with mobile development. Appium As a result, Tosca allows testing of native, hybrid, and web mobile apps across real iPhone, Android, mobile phone, and tablet devices without maintaining and possessing these devices. Just like the Testim product, the Tosca solution provides the ability for tests to be executed as part of CI/CD pipelines, allowing for the enforcement of shift left adoption. You can use Tosca to test on an iOS or Android phone directly, as well as through emulators or simulators available through an IDE like Android Studio. Using Tosca, you can scan your application and have it create test cases: Once Tosca has created the test cases, you can also create test cases of your own. One of the advantages of Tosca is that it allows you to write tests without having to write code. This is made possible through its library of modules that can simulate most every action, including opening a browser or filling out a form. In this example, we’ve used three modules for our Tosca mobile test case. We test: Opening a browser and hitting our app’s endpoint Selecting a particular type of vehicle Filling up a form about that particular vehicle Opening a browser and hitting our app’s endpoint Selecting a particular type of vehicle Filling up a form about that particular vehicle Notice that all we had to do is provide sample inputs (in the screenshot, we’re doing so for step 3 highlighted above). Once the test is complete, you’ll receive a diagnostics report on Tosca. Shift left for Mobile Testing By leveraging a product like Tosca, software engineers focused on mobile development can put their teams at a competitive advantage by employing shit left for mobile testing: Mobile features are validated during the development phase, similar to services and web frameworks. Tests are driven by a UI that can be expanded to internal consumers and product owners to help solidify their vision. The test suite can be built from a collection of “dry” modules that are structured to fully cover new features and functionality. Tests can be executed against an exhaustive inventory of mobile devices without owning and maintaining such devices. Before introducing new features to the primary code branch, the associated PR would have called the UI-generated tests in the same way unit or integration tests are executed in API or web framework CI/CD pipelines. Mobile features are validated during the development phase, similar to services and web frameworks. Tests are driven by a UI that can be expanded to internal consumers and product owners to help solidify their vision. The test suite can be built from a collection of “dry” modules that are structured to fully cover new features and functionality. Tests can be executed against an exhaustive inventory of mobile devices without owning and maintaining such devices. Before introducing new features to the primary code branch, the associated PR would have called the UI-generated tests in the same way unit or integration tests are executed in API or web framework CI/CD pipelines. Conclusion My readers may recall that I have been focused on the following mission statement, which I feel can apply to any IT professional: “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” - J. Vester “Focus your time on delivering features/functionality that extends the value of your intellectual property. Leverage frameworks, products, and services for everything else.” - J. Vester To reach peak productivity, software engineers focused on mobile development need to adopt shift left for mobile testing . However, the ideal choice should consider any associated learning curve and supportability when searching for solutions. mobile testing The Tosca product adheres to my personal mission statement by allowing teams to reach the shift-left state without additional source code to support and maintain. The product also allows non-engineers to participate in the test development, giving teams an advantage in ensuring designs match expectations. I’ve personally employed the shift left approach for several years and appreciate the experience every time a defect is avoided due to simply following the process. It’s time for mobile development to employ the concept of shift left. Have a really great day!

Why You Need to Shift-left with Mobile Testing

কেন আপনাকে মোবাইল টেস্টিংয়ের সাথে বামে স্থানান্তর করতে হবে

Warum Sie beim Testen mobiler Geräte den Shift-Left-Ansatz verfolgen müssen

Por qué necesita desplazarse hacia la izquierda con las pruebas móviles

Pourquoi vous devez passer à gauche avec les tests mobiles

मोबाइल परीक्षण के साथ आपको शिफ्ट-लेफ्ट की आवश्यकता क्यों है

モバイルテストでシフトレフトが必要な理由

모바일 테스트에서 왼쪽으로 이동해야 하는 이유

Por que você precisa mudar para a esquerda com testes móveis

Почему вам нужно сдвинуться влево при мобильном тестировании

Mobil Testle Neden Sola Kaymanız Gerekiyor?

Tại sao bạn cần Shift-left với thử nghiệm trên thiết bị di động

为什么你需要通过移动测试进行左移

Author:
(1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie);
(2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk);
(3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie);
(4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie);
(5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Table of Links Abstract and Introduction
HDR Standards
HDR Subjective Testing Workflow
Conclusion and References Abstract—Over the past few years, there has been an increase in the demand and availability of High Dynamic Range (HDR) displays and content. To ensure the production of high-quality materials, human evaluation is required. However, ascertaining whether the full playback pipeline is indeed HDR-compliant can be challenging. In this paper, we present a set of recommendations for conformance testing to validate various aspects of the testing workflow, including playback, displays, brightness, colours, and viewing environment. We assessed the effectiveness of HDR conversion techniques used in current standards development (3GPP) for making source materials. Additionally, we evaluate HDR display technologies, including OLED and LCD, using both consumer television and a reference monitor Index Terms—HDR, testing workflow, testing environment, playback, video coding. I. INTRODUCTION In recent years, HDR displays sales and content delivery have increased [1], [2]. Oftentimes, the displayed video output may not always be true HDR. It is hard to confirm whether the playback is actually HDR or to cross-check a playback pipeline disruption. Validating this is important for quality assessment studies and it yields technical challenges. The development of standards testing protocols for subjective viewing environments for HDR videos is being led by the Video Quality Experts Group (VQEG), International Telecommunication Union (ITU) Recommendations, and the European Broadcasters Union (EBU) for both televisions and broadcast applications [3]–[6]. Despite efforts made by previous authors to explore and adapt these techniques for HDR subjective studies [7]–[10], there are still issues unresolved. Particularly, the configuration and technical validation of the playback pipeline is often not transparently presented in the literature. Thus, there is currently a lack of an HDR quality assessment framework that can be readily used to conform with these standards. Taking into account all the above, in this paper, we introduce an HDR quality assessment testing workflow (Section III), which comprises three important elements, a) the documentation of the playback pipeline, b) the HDR intermediate file conversions, and c) the testing environment. This work was funded by DTIF EI Grant No DT-2019-0068 and The ADAPT SFI Research Center. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie); (4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie); (5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Author: Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie); (4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie); (5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Table of Links Abstract and Introduction HDR Standards HDR Subjective Testing Workflow Conclusion and References Abstract and Introduction Abstract and Introduction HDR Standards HDR Standards HDR Subjective Testing Workflow HDR Subjective Testing Workflow Conclusion and References Conclusion and References Abstract—Over the past few years, there has been an increase in the demand and availability of High Dynamic Range (HDR) displays and content. To ensure the production of high-quality materials, human evaluation is required. However, ascertaining whether the full playback pipeline is indeed HDR-compliant can be challenging. In this paper, we present a set of recommendations for conformance testing to validate various aspects of the testing workflow, including playback, displays, brightness, colours, and viewing environment. We assessed the effectiveness of HDR conversion techniques used in current standards development (3GPP) for making source materials. Additionally, we evaluate HDR display technologies, including OLED and LCD, using both consumer television and a reference monitor Abstract —Over the past few years, there has been an increase in the demand and availability of High Dynamic Range (HDR) displays and content. To ensure the production of high-quality materials, human evaluation is required. However, ascertaining whether the full playback pipeline is indeed HDR-compliant can be challenging. In this paper, we present a set of recommendations for conformance testing to validate various aspects of the testing workflow, including playback, displays, brightness, colours, and viewing environment. We assessed the effectiveness of HDR conversion techniques used in current standards development (3GPP) for making source materials. Additionally, we evaluate HDR display technologies, including OLED and LCD, using both consumer television and a reference monitor Abstract Index Terms—HDR, testing workflow, testing environment, playback, video coding. Index Terms —HDR, testing workflow, testing environment, playback, video coding. Index Terms I. INTRODUCTION In recent years, HDR displays sales and content delivery have increased [1], [2]. Oftentimes, the displayed video output may not always be true HDR. It is hard to confirm whether the playback is actually HDR or to cross-check a playback pipeline disruption. Validating this is important for quality assessment studies and it yields technical challenges. The development of standards testing protocols for subjective viewing environments for HDR videos is being led by the Video Quality Experts Group (VQEG), International Telecommunication Union (ITU) Recommendations, and the European Broadcasters Union (EBU) for both televisions and broadcast applications [3]–[6]. Despite efforts made by previous authors to explore and adapt these techniques for HDR subjective studies [7]–[10], there are still issues unresolved. Particularly, the configuration and technical validation of the playback pipeline is often not transparently presented in the literature. Thus, there is currently a lack of an HDR quality assessment framework that can be readily used to conform with these standards. Taking into account all the above, in this paper, we introduce an HDR quality assessment testing workflow (Section III), which comprises three important elements, a) the documentation of the playback pipeline, b) the HDR intermediate file conversions, and c) the testing environment. This work was funded by DTIF EI Grant No DT-2019-0068 and The ADAPT SFI Research Center. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

Recommendations for Verifying HDR Subjective Testing Workflows: Abstract and Introduction

Author:
(1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie);
(2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk);
(3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie);
(4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie);
(5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Table of Links Abstract and Introduction
HDR Standards
HDR Subjective Testing Workflow
Conclusion and References II. HDR STANDARDS The current HDR standard [11] for television and broadcast deployment of HDR content within the streaming media industry is hindered by the fact that this content requires a very-high bitrate for visual retention of cinematic and artistic intent in motion pictures. Since 2017, EBU’s Video System and Workflows working group have laid out various testing methodologies for HDR picture monitors. In 2019, EBU Tech Report TR047 [5] and 3320v4.1 [4] showcased the performance and recommendations for using first-generation HDR studio monitors. In 2022, Tech Report 3225v2 [6] expanded with more testing guidelines using specific HDR test patterns. In a similar timeline (2021), ITU published BT.2390- 10 [3], which explains Perceptual Quantizer (PQ) HDR TV architecture from content acquisition to display. Given the fact that current ITU subjective testing methodologies [12], [13] do not accurately account for HDR characteristics, different authors designed different methodologies for testing the quality of HDR videos [7]–[10]. Due to different test mechanisms and different interactions within the testing pipeline, it is unclear whether we can compare them. To wrap up, there are currently ongoing efforts from various standardisation bodies and researchers to define a methodology for the testing workflow of HDR videos. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie); (4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie); (5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Author: Author: (1) Vibhoothi,Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: vibhoothi@tcd.ie); (2) Angeliki Katsenou, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland & Department of Electrical and Electronic Engineering, University of Bristol, United Kingdom (Email: angeliki.katsenou@bristol.ac.uk); (3) John Squires, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: john.squires@tcd.ie); (4) Franc¸ois Pitie, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: pitief@tcd.ie); (5) Anil Kokaram, Sigmedia Group, Department of Electronic and Electrical Engineering, Trinity College Dublin, Ireland (Email: anil.kokaram@tcd.ie). Table of Links Abstract and Introduction HDR Standards HDR Subjective Testing Workflow Conclusion and References Abstract and Introduction Abstract and Introduction HDR Standards HDR Standards HDR Subjective Testing Workflow HDR Subjective Testing Workflow Conclusion and References Conclusion and References II. HDR STANDARDS The current HDR standard [11] for television and broadcast deployment of HDR content within the streaming media industry is hindered by the fact that this content requires a very-high bitrate for visual retention of cinematic and artistic intent in motion pictures. Since 2017, EBU’s Video System and Workflows working group have laid out various testing methodologies for HDR picture monitors. In 2019, EBU Tech Report TR047 [5] and 3320v4.1 [4] showcased the performance and recommendations for using first-generation HDR studio monitors. In 2022, Tech Report 3225v2 [6] expanded with more testing guidelines using specific HDR test patterns. In a similar timeline (2021), ITU published BT.2390- 10 [3], which explains Perceptual Quantizer (PQ) HDR TV architecture from content acquisition to display. Given the fact that current ITU subjective testing methodologies [12], [13] do not accurately account for HDR characteristics, different authors designed different methodologies for testing the quality of HDR videos [7]–[10]. Due to different test mechanisms and different interactions within the testing pipeline, it is unclear whether we can compare them. To wrap up, there are currently ongoing efforts from various standardisation bodies and researchers to define a methodology for the testing workflow of HDR videos. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. available on arxiv

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