The resulting video may then be shared via one or more social media outlets and online video platforms such as YouTube. The creators refer to themselves as &quot;vidders&quot;, their product as &quot;vids&quot;, &quot;fanvids&quot;, &quot;fanvideos&quot;, &quot;songvids&quot;, or the more recently adopted name &quot;edits&quot;, the act itself referred ...

Vidding - Wikipedia

FanVideo serves in the B2B, SaaS space. Let us walk you through our Companies database and other offerings. Designed with precision for VCs, PE, IBs, Incubators, M&amp;A, Innovation and more

FanVideo | Tracxn

We, at FanVideo, can turnaround an impactful video 680% times faster than industry standards.

Topical Videos | FanVideo

Having 10 or more reviews can increase ... 69%. On FanVideo, record your customers on a video call in 4K quality and get automagically edited videos with custom template, brand colours, logos, fonts and music. Helping DPS Warangal drive Paid Admissions to their school for Academic Year 2022. Neutrogena® makes awesome skin care products for normal, dry or oily skin. Almo is an Italian fashion brand that makes soft and breathable wearables. An education tech company engaged their ...

Customer Testimonials | FanVideo

Want 375% higher CTRs? Book a FanVideo demo today and scale your video journey.

FanVideo | Book A Demo | FanVideo

The fast-moving consumer goods industry faces a stiff competition and over 85% companies find it difficult to survive. Video testimonials can spread a good word-of-mouth and help escalate any brand&#x27;s presence.

Why FanVideo Is Right For FMCG Industry | FanVideo

Fintech companies have seen tremendous growth in the past decade especially with digitization of payments all across the world. FanVideo&#x27;s customer testimonials are a good way to escalate conversions.

Why FanVideo Is Right For FinTech Industry | FanVideo

FanVideo tool is empowered to create testimonials in an instant that helps you attract more customers

FanVideo - Product Guide | FanVideo

FanVideo helps you connect with your team members through effortless video calls that can be recorded, edited and hosted on social media pages to promote goodwill. ... Video stories can snowball into endorsements for your company. Not only do engaging and high quality clips grab attention but ...

FanVideo - B2B Case Study Video Story Template | FanVideo

Absolutely. On FanVideo, the teleprompter supports a variety of languages. You can cut-paste or type in your local language. A gaming company used our platform to record player testimonials in a mix of languages like English, Hindi, Kannada, Telugu, Tamil and Marathi.

FanVideo — Record, Create, Publish Customer Videos from Browser ...

FanVideo

fanvideo

Disclaimer: This article doesn’t contain any analytics or facts that should be construed as professional insight. It’s just for fun, entertainment, and to provoke nostalgia. Nowadays, NFTs (non-fungible tokens) are one of the hottest topics in the world of cryptocurrencies. We’ve seen them in numerous headlines. For example, mentions of Crypto Punks, Death Dip, Jack Dorsey’s first tweet, by Beeple and other projects which have become instant sensations and they have been on the rise in recent months. ”Everydays: The First 5000 Days” It is worth mentioning that the crazy popularity of NFT (which could be paintings, videos, musical works, GIFs, etc.) is met with high public interest and engagement due to the exceptional uniqueness that underpins NFTs. The interest of millions of people is one of the key factors for success in any sphere. What else attracts public attention on the internet on a global scale? Anything that requires a sense of humor and, of course, memes. But memes come and go, and old favorites find it hard to monetize their former popularity. Nevertheless, in 2021, they had an unexpected and very promising opportunity to make a comeback and be discussed again. Nostalgia is a wonderful feeling! Do you remember such images as  Disaster Girl, Overly Attached Girlfriend, Scumbag Steve, and Brian Loser? Sure, you’ve heard of them and have probably even shared them on your social networks. You may even have sent them to your friends for a laugh or to describe a situation without the need for any explanation. Having followed the NFT trend, the heroes of these memes have recently returned from the past. And not for nothing, as you can see, the forgotten stars have already made thousands of dollars by selling their memes for a lot of money. So, how much do NFT memes cost? Below, I have analyzed some of the unexpectedly revived images and the current auction prices of the three most popular NFT memes, thanks to (a global platform for business statistics) and the (a platform for artists and collectors that enables them to participate in the new creative economy). Statista Foundation NFT Marketplace 1. Disaster Girl Let’s start the analysis with the most profitable NFT meme of today: Disaster Girl. It seems to me that when one Saturday morning in 2005, Zoe Roth’s (the very girl from the photo) father took a picture of his daughter with a burning house in the background, he had no idea that he’d be launching one of the most iconic memes of the decade or that it would eventually become an NFT. “A few people have reached out to me and helped me understand what NFTs are”, said Zoe Roth, the main character of the meme, to publication in one of her interviews. By the way, Zoe also mentioned that ”this is the ideal meme for 2020 because it passed like it was on fire”. Know Your Meme On 18th April 2021, Zoe sold her NFT for 180 ETH at a to a user with the nickname @3FMusicAt. Now, this NFT is estimated at $472,000 dollars. That’s impressive, isn’t it? Foundation auction 2. Overly Attached Girlfriend “My name is Laina, and I am that (awful) face behind the meme. Possessing the authenticated ‘Overly Attached Girlfriend’ in NFT format, you are guaranteed to never be lonely. Never ever” - the quote in the token description on the Foundation platform. By the way, Laina Morris wasn’t at all excited about the format that made her famous in the first place. The meme appeared after a video was published on YouTube on 6th June 2012. At that time, Laina was participating in a contest that was conducted by Justin Bieber. He was looking for someone to create the song ‘Girlfriend’, to accompany his hit-single ‘Boyfriend’. Morris’ video went viral, receiving a response from millions of viewers because the fan video was very funny and also because the heroine was singing most of the song without blinking and constantly looking at the camera. Subsequently, people posted a clipped photo of Laina, adding funny signatures. This is actually what an overly attached girl looks like. Nevertheless, by selling the NFT meme for 200 ETH, the heroine earned even more than Zoe Roth. Now, this NFT is estimated to be worth approximately $480,000 dollars. 3. Grumpy Cat Even the most grumpy cat in the world, whose photo was published on Reddit in 2012 by the brother of the pet’s owner Tabatha Bundesen, found love and recognition in the form of NFT. It was sold on the Foundation marketplace for 44.2 ETH and is estimated to be worth almost $105,000 dollars at the time of writing this article. Initially, the meme gained insane popularity because it confused many people who could not believe that it was not photoshopped and that the cat really had such a snout. But the owner explained that their pet had an overbite and was born with dwarfism. The internet literally fell in love with the  Grumpy Cat, and in 2013, it was acknowledged as “The Meme of the Year”, according to Webby Award. The image was subsequently forgotten until it was converted into an NFT. By the way, the description of this NFT on the Foundation website ends with the phrase “Worst NFT Ever”, which perfectly matches the name of the viral image. Among other popular and expensive NFT memes are, of course, Nyan Cat, Bad Luck Brian, Leave Britney Alone, and Success Kid. The NFT trend has not only become one of the hottest topics in the world of cryptocurrencies, but it has also breathed new life into things that have been forgotten and lost their relevance 10-15 years ago. That is really amazing, isn’t it? Ex-stars of 2005-2012 memes have gained popularity again, due to the fact that they took the opportunity to follow the current year’s trends. People have earned thousands of dollars with once-forgotten photos. It should be noted that some of these heroes are planning to divert some of the money from their NFT sales to charity. Many collectors have come to think of memes as ‘art’ - unique cultural artifacts or the pop-art of today. This has spurred the development of a new market: the NFT meme market. However, its destiny still remains a mystery. Former stars can sell their NFTs riding on the wave of hype and rekindled popularity while earning interest on the subsequent resale. However, after a couple of months, they may be forgotten again. The second possible scenario in which events could develop is that the NFT meme exchange, in the long run, gains momentum and real value in the market. Moreover, it’s not just old memes that can be auctioned. Memes that are beginning to gain popularity in 2021 can also be auctioned. So far, the internet community has been fascinated by the absurdity of NFT memes, fueled by nostalgia and the return of old but well-known characters of funny images, as well as developing a desire to add such strange NFTs to their collections. ...What else are NFTs going to do in the near future? Let’s watch this space!

Top 3 Most Expensive Meme NFTs Ever Sold

Video gaming is one of, if not the biggest avenues of entertainment in current times. It has evolved into this mammoth industry that directly rivals the movie industry. The reboot of the popular PC game Call of Duty Modern Warfare raked . More people are playing more games than ever as newer trends have given rise to a whole new generation of ‘cyber-athletes’ and E-sports. People have taken to watching others play games so much so that E-sports might be an event in the Olympics by 2024. Almost every major tech giant is already in or trying to solve the puzzle of video games. This has grown into an ecosystem after the spread of the internet and mobile. But how big is it really? $600mn within just 3 days of its release The first video game was developed by a in the late 1950s — the same guy was a member of the team that built the first nuclear bomb. It was a 2D tennis game with just a single color. Since then, there have been revolutionary breakthroughs in both hardware and software. Processors have become so powerful that they’ve reached an threshold in advancements, signalling the . Graphic chips have given birth to an entirely new suite of AAA games with excellent graphics. The faster internet has connected gamers across continents and with newer F2P business models have hooked gamers in the recent years. physicist end of Moore’s Law Ecosystem Components Game Developers These are usually independent studios who develop games from the ground up. The writing team makes a story around which a design team builds the concepts that would tie the whole narrative together. The programming team codifies this logic into computer software along with additional code for capabilities like Online Gaming and Multiplayer. The major strength of a developer lies in the availability of the game engine at its disposal(made in-house or brought externally). Many game developers are owned by game publishers like DICE by EA which made Battlefield. Game Publishers /(Distributors) Much like the publisher of books, a game publisher is responsible for distribution of a ship-pable game. They generally finance the game development, often overseeing the design of the game by gauging consumer interest, prepare manuals, decide on packaging and marketing of the retail version of the games among various others. Many publishers like Activision Blizzard(NASDAQ : ATVI) own studios like Treyarch which made some of the Call of Duty games. Many publishers also distribute their own games by selling to retail stores(maybe through a distributor). Off-late much of the distribution happens digitally through platforms like Steam, Origin, UPlay, PSN among various others. App stores like Apple’s make it one of the top game publishers annually with even though it doesn’t make games! more than $9bn Hardware Developers They build the electronic infrastructure required to play games depending on the platforms and the function. The currently is mobile(mobile and tablets) which has major mobile phone companies like Xiaomi, OnePlus, Samsung, Apple etc. For PC Gaming( Desktop + Laptop), Intel makes processors(CPUs), Nvidia makes graphic cards(GPUs) and other hardware like monitors, cases, hard drives are made by a plethora of more than 1000s of companies. biggest platform Last but not the least are consoles majorly 3 — Playstation by Sony, XBOX by Microsoft and Wii by Nintendo. Nascent stage platforms include AR and VR, Oculus owned by Facebook being one of the companies. Keyboards, mouses, headphones and controllers used to play games are made by companies like Razer, Steelseries etc. Software Developers Additional software is usually required to make the game work as intended often requiring anti-cheat systems like Punkbuster to ensure a rewarding gameplay experience. Communication software for gamers is developed by companies like Discord ( ). valued at $2bn There may be an overlap here with the game publishing aspect as products like Valve’s Steam which was a breakthrough in digital game distribution, is a software developer and also makes tools for movie animations. are at the heart of the game and built by stalwarts like Unreal Engine which was used in two popular battle-royal themed games Fortnite and PUBG, Unity Engine, Source Engine etc. Over the last 2 years Elon Musk backed AI company OpenAI made tremendous progress and has garnered much fame after it managed to beat a team of 5 professional DOTA 2 players convincingly. While plans are not yet known about the use of this product, maybe Musk has his eye on the of major annual gaming events. Game engines grand multi-million prize pools Streaming services These are services that allow a user to livestream any game or activity they might be involved. Primary companies include Twitch owned by Amazon and Youtube Gaming. Streaming generated an enormous amount of content compared to movie and music industry. This streaming culture has given rise to celebrities who often earn money through sponsorships and fan donations. Netflix has said that they in one of their earnings reports. More people are watching their “stars” play their favorite games and this has taken away a noticeable amount of screen time off traditional media like TV and Films. With more than 15mn Daily Active Viewers, 2mn Daily Streamers, 40K concurrent streamers and 1mn concurrent viewers, there is an immense potential for services like these to seriously consider advertising. compete more with Fortnite than with HBO All in all, streaming services have boosted engagement in the gaming community and thus are looking to improve retention amid threats from companies like Netflix and Apple TV+. Business Dynamics The gaming industry underwent an explosion in terms of size and reach in the 21st century. This was a result of the concerted reaction between developments in technology and the ability to monetize these games by improving on every aspect of the process. Major drivers are as follows : Internet boom As the reach of internet becoming wider, with greater penetration at a rapid scale, along with higher speeds and lesser cost, the world got smaller than before. It opened an abundance of opportunities and use cases in both business and technology. Multiplayer gaming became popular and this re-engaged those gamers who got bored after completing the single player campaign and were left to wait until the next ‘good’ game was released. Internet had changed the value chain of the industry and also gave rise to the world of ESports. MENA markets like Saudi Arabia have mobile internet penetration over 90% and is one of the fastest growing opportunities. Community building Gaming has a simple retention hook to keep you coming back for more — a dopamine hit. Each time you play something, the objective of the game designer is to make it as satisfying as possible. As they started making better games, more players got into the scene and also brought their friends along(often bunking school to “pwn some noobs”). The virality here is high in cases where the game becomes a popular brand like DOTA, CS, COD, LOL etc. As the skilled players came together sometimes moving across continents to form teams which competed amongst themselves in gaming tournaments which later evolved into ESports. The best teams often command celebrity statuses along with a huge fan following. Investors are now looking into sponsoring teams as there seems to be a huge business potential. One of Europe’s biggest football clubs PSG partnered with a Chinese team, LGD a while ago. Apart from fans and players, the community also features cosplay models and independent artists who participate in comic-con events and design in-game cosmetics respectively. Mobile boom When Steve Jobs introduced the iPhone in 2007, he gave a glimpse of what the future of mobile would look like. With the launch of Android in 2008, that dream eventually started shaping into reality as games like Angry Birds and Candy Crush got extremely popular. As Google perfected Android with every iteration, the Chinese companies started building world-class mobile phones that rivaled the Apple and Samsung and were priced significantly lower. With e-commerce playing catalyst, the spread of devices became faster and this brought more people into the ecosystem. Gone are those days when you had to save up to go to a video game store and get your hands on the latest copy of GTA. The value chain of the industry has changed from offline to online as major components of the chain like distribution and service became digitized. Shift in meta and business models Meta is a term that refers to a ‘trend’ in gaming vocabulary. As internet became faster and more accessible, game developers came up with new genres to leverage on the increased player base.one of them being , which has displayed exponential growth in number of gamers. PUBG, Fortnite, Overwatch, Apex Legends are the ones that dominate the market currently. With the accelerated adoption of ‘Freemium’ business model, developers devised innovative ways to make money by introducing and which were usually not one time purchases. Battle Royale in-game currencies season passes What Lies Ahead Over the next few years, technological improvements overall will lead to a much more robust ecosystem but these are a few things in the hype currently. Game Streaming a.k.a Netflix for Games The concept here is simple — to be able to play games on any device (mobile, tablet, PC, Console) without having to worry about installation or hardware requirements anywhere. The game will run on the and will be streamed to the user over the internet. While this tech is far from polished, Google has launched Stadia with support to 22 games. Over the next year, there’s going to be quite a lot of activity in this space and tech giants are preparing to take a piece of this market. Microsoft’s xCloud(XBOX on the cloud) will begin testing its services in 2020 in countries like Canada, Japan and India. cloud Hardware Battle Even though a lot of development is being focused to game streaming, the battle for platform will still continue for some time. With Sony and Microsoft looking to launch their in 2020 and franchisee exclusivity for games like Red Dead Redemption, Uncharted, God of War, a single hardware platform will take more time to establish itself in the market. next-gen consoles Esports in Olympics Esports is getting bigger every year and Esports monetization to reach $3bn by 2022. It will rival other professional sports like Football, Basketball and Cricket with more spend per viewer. Intel has already taken a step further and is gaming event next year. Goldman Sachs predicts hosting an olympics-sanctioned

The Gaming Ecosystem Explained

Founders at Work: 150+ YCombinator Founders’ Stories

Gaming is a serious business and can be taken very seriously by any gamer that's been playing for years. But even the most serious players appreciate a good meme. Besides, memes are often used as a way to communicate with other gamers, and they can be a great way to relieve stress after a long day of gaming. Without further ado, here are the 25 gaming memes that will make any gamer LOL. 1. Godfather Production Meme Credit: Mandatory 2. Character Editor vs Cutscenes Meme Credit: Mandatory 3. The Crystal Meme Credit: Mandatory 4. Mario Kart Meme Credit: The Gamer 5. Stealth Games Meme Credit: me-gamescon.com 6. Nintendo Switch Meme Credit: Gamerant 7. Mom and Console Meme Credit: Gamerant 8. Friendly Fire Meme Credit: ifunny 9. Final Boss Meme Credit: Pinterest 10. Violence in Games Meme Credit: What To Laugh 11. Final Quest Meme Credit: Gamehag 12. Random NPC with Quests Meme Credit: Funvizeo 13. Dialogue Options Meme Credit: Niche Gamer 14. Used Video Games Meme Credit: Reddit 15. Game Loading Meme Credit: World of Warships forum 16. Fake Demos Meme Credit: Mandatory 17. Forgotten NPC Meme Credit: Mandatory 18. Lens Flares Meme Credit: Mandatory 19. Having Different Consoles Meme Credit: Mandatory 20. Customized Character Meme Credit: bestofgamingmemes 21. The Theme Song Meme Credit: Reddit 22. Electronic Arts Meme Credit: Reddit 23. Ramming NPC Meme Credit: Reddit 24. Getting Serious Meme Credit: Reddit 25. Desert Map Meme Credit: Reddit Memes are a great way to relieve stress and communicate with other gamers.  No matter what game you are playing, there is sure to be a meme to fit the situation. I hope you enjoyed this collection of memes. Be sure to check out more gaming memes from Hackernoon: The 25 Funniest Warzone Memes on the Internet The 25 Funniest Roblox Memes in 2021 25 Best Fire Emblem Three Houses Memes

25 Best Gaming Memes that Will Make Any Gamer LOL

It’s difficult to say how many digital photos people produce every year, but the total number is estimated at more than 1 trillion per year. That huge amount of photos mostly come from mobile phones and quite often these images are stored in JPEG format. Apart from that, many industrial cameras also generate jpegs in huge volumes. Image extension .jpg is the most frequent choice, and this is actually a default setting in many smartphones and cameras. Abbreviation JPEG means both image format and lossy compression algorithm which is utilized for image encoding and decoding. JPEG means , which created the standard. The first draft of JPEG standard was released in 1992. JPEG standard specifies the codec, which defines how an image is compressed into a stream of bytes and decompressed back into the image. It is not a brand new algorithm, but this is solid ground and very popular method to store compressed images. Let’s have a look why it could be a good solution and how it’s actually working. Joint Photographic Experts Group algorithm is always lossy, which means that we don’t store full data from an original image. This is not actually a problem — algorithm removes image detail which most of the people just can’t see. That method is called “ ” to emphasize that level of image quality losses could be really low. In most cases, we can get compression ratio in the range of 10–12 times, which saves us a lot of HDD/SSD space. JPEG image compression visually lossless compression This is an example of extreme image quality losses with JPEG compression. You can see original Lena image in TIFF format (512x512, 24-bit, 769 kB, no compression) and the same image at JPEG format with quality compression coefficient 50%, subsampling 4:2:0, 24-bit, image size 23 kB. Can you see any noticeable difference at 100% scaling? Please note that this is an extreme case with compression ratio ~33, though, for visually lossless JPEG compression, the recommended value is around 10–12. Original Lena image, 769 kB Compressed Lena image, 23 kB JPEG compression and decompression used to be considered computationally intensive and slow. An idea about was far from being true. Since then, new hardware and new approaches for parallel programming made JPEG fast, reliable and widespread. fast image compression The general approach to image compression implies the following stages Color transform from RGB to YCbCr together with a shift Sub-sampling and partitioning Transform to a frequency domain Removal of high-frequency detail from the image Reordering for better compression Removal of zeros series Lossless entropy coding to remove some more extra data Final packing Color transform from RGB to YCbCr That transform is based on our physiological experience. The human visual system can perceive minor changes of brightness, though it’s far less responsive to changes of color (chroma components of the image) for the regions with the same brightness. That’s why we can apply stronger compression to chroma to get less image size of the compressed image. We take an RGB image and convert it to luma/chroma representation in order to separate luma from chroma and to process them separately. Luma is usually called Y (intensity, brightness) and chroma components are called Cb and Cr (these actually difference Cb = B — Y and Cr = R — Y). That transform is done at the same time with data shift to prepare data to processing stage which is called DCT (discrete cosine transform). Subsampling and partitioning As soon as we can consider chroma components to be less important than luma, we can decrease the total number of chroma pixels. For example, we can average chroma in horizontal or vertical direction. At the most extreme case, we can average 4 neighbor chroma values in the rectangle 2x2 to get just one new value. That mode is called 4:2:0 and this is the most popular choice for subsampling. For further processing, we divide the whole image into blocks 8x8 for luma and chroma. That partitioning scheme lets us process each block independently, though we will have to remember coordinates of each block which are essential at image decoding. DCT: Discrete Cosine Transform DCT is a Fourier-related transform which is similar to the Discrete Fourier Transform (DFT) but using only real numbers. You can get more info at . Actually, we apply that 2D transform to each block 8x8 of our image. The main idea is to get other data representation and to move from spatial to a frequency domain. The result of DCT is data array in a frequency domain and this is a very clever step to work further not directly with luma and chroma, but with frequencies of luma and chroma from our image. Big objects on the image are considered to be low-frequency data, though small/tiny objects are considered to be high-frequency elements. Wiki In the new block 8x8 the upper left element is called DC (this is average value for all pixels from the original block), and all other elements are called AC. If we compose a new image from DC elements of each block, we get original image with reduced resolution. New width and height will be 1/8 from the original image. This is just an illustration of how DCT is working and what’s DC and AC. It could sound strange, but at this step, we don’t have any data reduction. Not at all. On the contrary, after DCT we get more data in comparison with data size on the previous step, but still, this is a very important action. New representation soon will let us achieve strong compression, but not right now. We do need some patience and it will be rewarded shortly. Quantization and reordering We’ve come to the point where we have to introduce some data losses. That stage is called quantization. At first, we create a special quantization matrix 8x8 with coefficients. At upper left part of the matrix these coefficients are equal to 1 or more, but towards lower right part they are getting bigger. Quantization means division of each value from 8x8 block to corresponding coefficient from quantization matrix. After such a division and rounding, we get reduced values for each 8x8 block and the most important issue here is that we get many zeros for values which are close to right bottom part of the block. Quite often we can see some areas of the block which are filled with zeros. That is exactly what we hoped to get — series of zeros. This is the way how quantization detects high-frequency elements to be discarded. For further processing, we apply the so-called Zig-Zag algorithm to create a linear array of values from the block. This is done via a zigzag-like path when we start to move from the upper left corner of the block and go to the right bottom corner. After such reordering, we get a series of values for each block 8x8. We will not work with blocks now, we will work with these reordered data. After quantization we not only have less data, we’ve introduced some losses to original image. Most losses at JPEG compression algorithm comes here. That’s why the choice of quantization matrix is the key to get acceptable image quality of the compressed image. JPEG standard doesn’t define such a matrix and many camera and software manufacturers apply lots of efforts to develop the best possible solution. DC coding Starting from that point we will process DC and AC elements separately. We start from delta coding for DC. We just take the first value from each block (this is DC component) and store the difference with DC value from the previous block. This is very simple and straightforward. The thumbnail, which is composed of DC components, could be used as a downsampled version of the original image. RLE: Run Length Encoding Starting from here, we will work with AC elements only and we remember that each block consists of 63 such values. Actually, we’ve come to the point where we finally can do data reduction. In each set of AC elements we can see series of zeros and now we can substitute them with some shortcodes which could store the same information. This is the lossless algorithm and we don’t introduce any image losses here. RLE method transforms sequence of values to sequence of pairs. The first element of the pair is called symbol, the second element of the pair is non zero value. For each series, we code in the symbol the number of preceding zeroes and a bit length of the non-zero value. The idea of RLE is to store at just one value the number of consecutive zeros which we see before the next non-zero value from AC data. Here we have great data reduction and this is lossless transform! This is the way to shrink all series of zeros that we have among AC elements. But that is not all, we can get some more compression. Huffman coding That lossless compression algorithm is named after Huffman which was the inventor of that method. It’s also called entropy coding algorithm and here it’s applied to get better compression after RLE. The idea of that algorithm is to compare all codes that we get after RLE and to choose the shortest representation with fewer bits for those codes which occurred more frequently. At Huffman stage, we compute the frequency of each symbol and create optimal bit code for each one. We will not get into more detail here, you can see full info at . Wiki Final packing Having finished with compression, we need to pack compressed data from all blocks, to add the correct header for jpg, to set file name, and to store compressed file to HDD. Each photo camera and smartphone does pretty much the same. We just had a look how it goes several billion times every day worldwide. Tips and tricks JPEG algorithm was created to compress real photographic images. It’s not good at artificial image compression, for example for images with text. If we try to compress such an artificial image with JPEG, the result is not bad, but that compression algorithm just wasn’t created for such a task. Standard JPEG compression, which is based on DCT, can’t be lossless by definition. Even if we define compression quality to be 100% (which means no quantization), we still get some minor losses due to Color Transform and DCT, because after DCT we get float values which have to be converted to integer values, so we apply rounding and this is lossy operation. Please note that Lossless JPEG exists, but this is totally different algorithm, though with the same name. When your software is asking you to save your image to JPEG, please note that there are many ways to define the quality parameter, so suggested value could differ from your expectations quite a lot. Usually, we utilize JPEG compression quality parameter which is in the range of 0–100%, but for real life, it’s not less than 50%. Visually lossless JPEG compression is considered to be the case for quality 90% and more. To check it visually, you can try to discover slight rectangle borders at 100% zoom. If you don’t see them, it means that your compression is visually lossless for you at your viewing conditions, which is good. JPEG standard allows so called restart markers which are built in jpeg bytestream and they are intended to offer much faster JPEG decompression. Nevertheless, most cameras and software produce jpeg images without restart markers. You can check the number of restart markers in your jpegs with JpegSnoop software. Jpegtran utility can help you to insert the desired number of restart markers into your jpeg images. Some software manufacturers utilize their own units for compression quality like “jpg for web” or “quality level from 1 to 12”, so you need to be prepared to check that. The best way to prove such a compliance could be JpegSnoop software which can show you the real value of compression quality for luma and chroma in standard units together with quantization matrices for luma and chroma. If you need to check internals of your JPEGs and do encoding or decoding very fast, please have a look at the following links: — very useful software to check what’s inside your jpeg images, including all meta tags and internal parameters: JpegSnoop https://github.com/ImpulseAdventure/JPEGsnoop (the fastest solution for JPEG encoder and decoder on GPU): Fast image compression on CUDA https://www.fastcompression.com/ This article was created in cooperation with CEO and Founder of Fastvideo. Design of high performance imaging solutions on CUDA ( ) Fyodor Serzhenko — Fastcompression.com

Why Do We Need JPEG Compression and How It’s Technically Working?

Videos have become very popular in the current era of the Internet with applications ranging from Video-on-Demand to live streaming to virtual reality. As the demand for high quality video continues to grow, it's important to ensure that they're delivered efficiently at a low bandwidth. AV1 is an open video compression format that was developed by the Alliance of Open Media. In this blog post, I will be going over some of the core tools that are used in AV1. I will be using to show the tools as well as some of the things that I am currently working on in this summer. My main work included adding compound types and film grain work. AOMAnalyzer GSoC The Internet and Videos The Internet that we know and love is filled with videos (of mostly cats probably). There is a significant demand for videos with applications such as Video on Demand, streaming, real-time communication and many others. A showed that over 60% of the internet traffic comes from videos and this will continue to increase going forward. recent study by Sandvine Raw Videos Consider this YouTube video I watched the other day. It's a 30 second video recorded at 8K. Let's do some math to see the size of the raw video I was watching. Resolution: x pixels
FPS (Frames Per Second): Video Length: seconds ( frames)
Pixel Size (RGB) : bits ( * ) 

Total Size: * * * * ~= gigabits
Roughly gigabytes 7680 3840 30 30 900 24 8 3 7680 3840 30 30 24 637 80 A 30-second video is bigger than the size of If you were to stream this video, you would need to have a bandwidth of . That's crazy and almost impossible for most internet connections! Yet in reality, when I downloaded the 8K video, the size of it was just 35 megabytes. What exactly is going on? GTA 5! 21 Gbps The World of Video Codecs The answer has to do with . Essentially, these are tools that compress raw videos into a smaller size (encode) and decompress while viewing them (decode). So most of the videos that we are watching have been compressed and are usually lossy, which means that some information was lost during compression (usually hard for us to notice with the eye). Thus, when we reduce the quality of a video, we ask the encoder to compress even more and thus more information is lost. It's also why when you re-upload a video again, the video gets even more distorted than the previous one. video codecs and compression There's a lot of cool things that take place when compressing a video and . I want to touch on some of the tools being used and discuss the work that I am currently doing with AV1. It mostly revolves around extracting metadata from the decoder and making use of it. It's very useful for analyzing/debugging for codec researchers and engineers. One main use of extracting metadata is to create a bitstream analyzer. you can learn more about it here AV1 and Royalty Free Codecs Codecs were used for quite some time even way before they existed. They were used during times when software was mostly proprietary. They were used during the days when movies and videos were stored in CDs and you had to buy/rent them. Thus, video codecs were heavily patented and using them involved paying royalty fees. But the internet needs an open standard for video compression now more than ever given the accessibility of videos everywhere. There have been efforts in making open and royalty-free encoders. There was a ton of work done by Google with , Mozilla with , Cisco with and many others. Eventually, all these companies teamed up and formed a non-profit consortium known as to develop open, royalty-free media tools. VP9 Daala Thor Alliance for Open Media One of the coding formats developed was AV1, which was open and royalty-free. The work of VP9, Daala and Thor were all combined to create this codec. Currently, the widely available open source encoders are , , and , while the most popular decoder is . rav1e libaom SVT-AV1 dav1d As we will see, there are a lot of tools being used in AV1. Choosing the right tool to use for a video is quite complicated. In fact, choosing the best tools may not be feasible at all and can take forever. Thus, there are heuristics and pruning that take place and that depends on the implementation of the encoder. As a video codec engineer, it's important to be able to visualize and see the tools being implemented. One of the ways to do that is by using a codec analyzer. Bitstream Analyzer As mentioned, there's a lot of research that goes into video codec, so being able to analyze the components of the encoder is very useful. This is what a bitstream is used for and it allows you to display the different components of the bitstream such as block partitioning, motion vectors and many other details. The analyzer I am working with is , which is a browser-based analyzer for viewing AV1 bitstreams. It is quite unique because of the fact that it's free to use and all you need is a browser. It mainly consists of two components: The decoder, which is a js/wasm decoder that decodes the video and extracts the relevant metadata, and the UI which displays the metadata. AOMAnalyzer The decoder currently comes from , which also extracts the relevant metadata for the analyzer. Although it's written in C, there is a way to build it with . This will generate the necessary js/wasm files to be used for the AOManalyzer. You can find instructions to build it libaom Emscripten here The UI is a React/Electron project and the source code can be found . You can also access the built version . So if you wanted to analyze a video, you would supply the file as an argument in the URL pointing to the AV1 encoded file. here here As an example, here's a link to a sample bitstream: https://beta.arewecompressedyet.com/analyzer/?maxFrames=4&decoder=https://media.xiph.org/analyzer/inspect.js&file=https://mindfreeze.tk/~mindfreeze/ducks_1.00_23.ivf The arguments are the following: Link to the js/wasm decoder that decodes and extracts the metadata decoder: Link to the AV1 bitstream file to analyzer file: Number of frames to decoder. We may need this since decoding generates a lot of data for the browser maxFrames: Looking through the Analyzer One of the ways you can learn about how encoded videos work is by looking at the videos through the lens of the analyzer. Currently, AOMAnalyzer supports the major coding tools used in AV1 such as block partitioning, motion vectors, transform types, inter/intra prediction, and many more. Let's take a look at a sample sequence: Block Partitioning The first step to compressing a video involves partitioning the frame into different blocks, sub-blocks (sub-sub-blocks, ...). This allows us to work on different parts of the frame and do different things on them. For example, areas that have the same color throughout have bigger partitions since the area is roughly the same, whereas detailed areas such as the player's jerseys have tiny sub-blocks since they have a lot of detail. Let's take a look at the split mode on the analyzer: You can see here that there are big blocks around plain areas such as the field, and smaller blocks in the players' jerseys. AV1 supports multiple block sizes ranging from 4x4 all the way up to 128x128. There's support for rectangular blocks as well. A frame can be partitioned in a lot of ways and the way you partition it is super important because that's the level in which different components will be applied. The more partitions you have, the more time and bits you spend encoding, and if you have a deadline to meet (such as real-time video or video size), you may not be able to encode in time. Thus, it's important to choose the right block partitions. Inter/Intra Prediction Normally for a video, there's a lot of repetition that takes place. For example, consider the sequence of the first 3 frames in the football video: We can see that there's a lot of repetition here. There is a slight movement in the players preparing to kneel down. They all have the same green grass background. There are few things we can consider doing here: Instead of storing the whole frame, why don't we have estimations of plain areas instead of all the pixel values there? Moreover, why don't we just store one main frame and predict/keep track of the movements taking place? After all, videos usually have some form of continuity that takes place throughout the sequence. This is where inter and intra prediction comes into play. For the above sequence, suppose the camera doesn't move but the players move. One thing we can do is instead of displaying another copy of the frame, we can just track the movement of the players. This is where inter prediction comes in handy On the other hand, consider one of those big blocks that's just mostly green. Instead of storing all the pixels for that block, we can estimate the color of the block and just say that the color of that block is just that. This is where intra prediction comes in handy. There are different types of frames in AV1 but essentially, they boil down to two main types: Intra Frame (keyframe) and Inter Frames (motion estimation). Intra Prediction: Spatial Redundancy If we take a look at the football game, there are a lot of areas that are correlated, such as the green field. There are some blocks, where the color of the block is roughly the same (greenish for example). Instead of storing pixel values for each point, we can store some correlation metadata that estimates what the pixel values will be. AV1 has many modes for intra prediction such as Directional Intra Prediction, True motion, Chroma from Luma, and many others. Essentially, edge pixels of the corner block are used to predict the contents of the block. The mechanism used to predict the blocks depends on the mode chosen. You can see the different types of intra prediction modes used on AOMAnalyzer by choosing the mode layer. Inter Prediction: Temporal Redundancy There's a lot of motion that takes place in a video. Inter prediction, tries to predict the motion of moving object by using multiple frames. Motion vectors are used to determine the new position of the block from the reference frame. For example, in the case of football players, there is little movement in every frame. Thus, the encoder tries to predict the motion of the blocks by comparing frames. The figure below shows what motion was predicted for the second frame on AOMAnalyzer. In order to predict the motion of the block, we need a reference frame to extract the motion. In AV1, you can either use one reference frame (from the past to predict the current frame), or you can use two reference frames. When two reference frames are used, they can either both be from the future/past (unidirectional) or one is in the past and the other is in the future (bidirectional). Compound Prediction In the case where 2 reference frames are used, the prediction values for each reference frame will be used to generate the final motion vector predictor. The equation for predicting the value in compound prediction is: where is the weight distribution for and , and represent the pixels at position in the two reference blocks, and is the final predicted pixel value to be used. Since is a float and floats are slow for computation, the actual equation is scaled up by 64 for integer computation and . 0 ≤ w(x,y) ≤ 1 R1 R2 R1(x, y) R2(x, y) (x, y) P(x, y) w(x, y) 0 ≤ m(x,y) ≤ 1 In AV1, there are three modes for choosing the weight distribution: : In this mode, the weight is equally distributed between the two reference frames. Thus, or COMPOUND_AVERAGE w(x, y) = 0.5 m(x, y) = 32 : In this case, the distance between the current frame and reference frames ( and ) is taken and the weight is determined based on the relative values of the distances. In this case, is a piecewise constant function by comparing the distances and COMPOUND_DIST d1 d2 m(x, y) d1 d2 : In this case, the pixels are combined when the difference in pixels between the reference frames is very small. In the case, where the difference is large, one of the reference frames is prioritized and the pixel is weighted more towards that side COMPOUND_DIFF : In this mode, the weight is determined based on a set of 16 shapes that have been preset. These shapes split the block into two sections with different angles. In the figure below, you can see the different wedge shapes that can be used. Thus, more weight is given to one of the reference frames in one region, and more weight is given to the other reference frame in the other region. A wedge sign is used to indicate which region corresponds to which reference frame. COMPOUND_WEDGE In AOMAnalyzer, you can analyze the compound type used for each block by selecting the compound type. The figure below shows the compound types used for a frame. Furthermore, you can also analyze the wedge shape and sign used when COMPOUND_WEDGE is used. Film Grain Synthesis Sometimes, when recording in low light, we notice that the video is grainy. While we may want to get rid of the grain, there are cases where we need to preserve it due to its style. As described above, video compression depends a lot on spatial and temporal redundancy. As you can imagine, film grain is quite the opposite due to randomness. It changes drastically with time and is randomly different across the frame. Thus, preserving and compressing grain is quite difficult for encoders. In fact, some of the codec tools used in compression can suppress and remove film grain. Instead of preserving film grain, another approach would be to generate artificial grain. AV1 has a tool that allows you to generate synthetic grain and add it to the video. Prior to encoding, the user can first detect the grain, denoise the source video, determine the film grain parameters and pass them to the encoder along with the input video. The encoder then generates film grain parameters that the decoder uses to add the grain once the video is decoded. The figure below shows the step in which film grain is generated and added. Quick Tangent: How to use film grain in AV1 Let's say you have a grainy video and wanted to preserve film grain. Assume it's a YUV file with height h and width w. Denoise your video: You can use ffmpeg to denoise your video. There are multiple ways of doing that. As an example, here's a command using hqdn3d filter ffmpeg -vcodec rawvideo -video_size wxh -i input.yuv \
 -vf hqdn3d=5:5:5:5 -vcodec rawvideo -an -f rawvideo \
 denoised.854_480.yuv Once you denoise the video, you will need to estimate film grain parameters. libaom currently has an example noise model that models the noise between the source and the denoised video and generates the film grain parameters. Once built, you can run the following command: ./examples/noise_model --fps=fps --width=width --height=height --i420 \
 --input-denoised=denoised.yuv --input=original.yuv \
 --output-grain-table=film_grain.tbl You can then encode your denoised video using an AV1 encoder and pass the film grain table as a parameter. Here's an example of encoding using libaom: aomenc --cpu-used=4 --input-bit-depth=bit-depth \
 --i420 -w W -h H --end-usage=q --cq-level=25 --lag-in-frames=25 \
 --auto-alt-ref=2 --bit-depth=bit-depth --film-grain-table=film_grain.tbl \
 -o denoised_with_grain_params.ivf denoised.yuv How Film Grain Synthesis works The film grain synthesis is a "pseudorandom" process. This is because one of the film grain parameters is the random_seed which ensures that the grain pattern is the same if the process is repeated The film grain pattern is generated in a raster scan order using an . The parameters of this model are specified by one of the film grain parameters, ar_coeff_<luma, cb, cr>, which is a list of AR coefficient values. The ar_coeff_lag parameter determines the neighborhood range of values to pick from the previous blocks. For each pixel at position $(x, y)$, the grain value is the following: autoregressive (AR) model where are the AR coefficients and is a unit-variance Gaussian noise. The figure below is an example of how the grain value at is calculated with the ar_coeff_lag = 2: a0 , ... , aN z (x, y) This process will generate a 64X64 grain block for luma and a 32X32 grain block for chroma blue/red. Since each grain pixel depends on the previous pixels, the blocks are initially padded so that the top-left corner blocks can have proper values. Once the grain samples are generated, the grain is applied to the frame in blocks of size 32X32 for luma by taking a random subblock of 32X32 from the grain block and applying it to the frame block. For chroma blue/red, the size of the subblock depends on . In the case of 4:2:0, the size would be 16X16. For 4:0:0, the size would be 8X8. chroma subsampling The final pixel position at will be: (x, y) where is the denoised pixel value, is the grain block value, and is a piecewise-linear function that scales the grain sample according to the pixel intensity. The function is generated from the scaling points provided by the film grain parameters. For luma values, the value is just the pixel value . However, for chroma blue/red, the value is the following: P(x, y) G(x, y) f f t P(x, y) t where is the average of the collocated luma pixels and , , and are values specified by the film grain parameters. avg(P(x, y)) b_u d_u h_u Analyzing Film Grain through AOMAnalyzer I am currently working on adding Film Grain analysis to AOMAnalyzer. Currently, you are able to see the what the film grain parameters are, as well as the scaling function for all 8-bit pixel values. You are also able to see the 64x64 luma grain sample as well as the 32x32 chroma grain samples that were generated from the film grain parameters. Moreover, you can also see what the grain image looks like for luma, and chroma. f Conclusion There are still a lot more coding tools being used in AV1 such as Transform Coding, Entropy Coding, In-Loop Filters and much more. There's also a lot of work left to do for GSoC in the analyzer. In the next blog post, I will discuss the tools used in AV1 and visualize them with AOMAnalyzer. Also published here.

How Video Compression Works: A Deep Dive into AV1

What we’re building We’ll see how to build a flutter app for iOS/Android that allows users to view and share videos. In another post I showed how to do this with as our video storage API. In this tutorial we’ll use to host the videos instead. We’ll also add client-side encoding and HLS support, so the client can stream the videos with adaptive bitrate. Publitio Firebase Cloud Storage The stack - For making cross platform apps. Flutter - For storing video metadata (urls) and syncing between clients (without writing server code). Firebase Cloud Firestore - For hosting the actual videos. Firebase Cloud Storage - For running client-side video encoding. FFmpeg Why client-side encoding? In most video workflows there will be a transcoding server or serverless cloud function, that encodes the video into various resolutions and bitrates for optimal viewing in all devices and network speeds. If you don’t want to use a transcoding server or API (which can be quite pricey), and depending on the kind of videos your app needs to upload and view, you can choose to forego server side transcoding altogether, and encode the videos solely on the client. This will save considerable costs, but will put the burden of encoding videos on the clients. Even if you use some server-side transcoding solution, you’ll probably want to perform minimal encoding on the client. The raw video sizes (especially on iOS), can be huge, and you don’t want to be wasteful of the user’s data plan, or force them to wait for WiFi unnecessarily. do Video encoding primer Here is a short primer on some terms we’re going to use. X264 Codec / Encoder This is the software used to encode the video into the H.264/MPEG-4 AVC format. There are many other codecs, but seeing as the H.264 format is currently the only one that is natively supported on both iOS and Android, this is what we’ll use. Adaptive bitrate streaming A method to encode the video into multiple bitrates (of varying quality), and each bitrate into many small chunks. The will allow the player to choose which quality the next chunk will be, according to network speed. So if you go from WiFi to cellular data, your player can adapt the bitrate accordingly, without reloading the entire video. streaming protocol There are many streaming protocols, but the one that’s natively supported on iOS and Android is Apple’s - HTTP Live Streaming. In HLS, the video is split to chunks in the form of files, and an playlist file for pointing to the chunks. For each quality, or , there is a playlist file, and a master playlist to rule them all 💍. HLS .ts .m3u8 variant stream Configurations Installing flutter_ffmpeg We’ll use the package to run encoding jobs on iOS/Android. requires choosing a codec package, according to what you want to use. Here we’ll use the package, as it contains the x264 codec, and can be used in release builds. flutter_ffmpeg flutter_ffmpeg min-gpl-lts Add the following to your : android/build.gradle ext {
 flutterFFmpegPackage = } "min-gpl-lts" And in your replace this line: Podfile pod , : => File. (symlink, ) name path join 'ios' with this: == pod + , : => File. (symlink, ) pod , : => File. (symlink, ) if name 'flutter_ffmpeg' name '/min-gpl-lts' path join 'ios' else name path join 'ios' Cloud Storage configuration If you’ve already setup , as discussed in the , then you just need to add the cloud_firestore package. firebase in your project previous post Now we need to configure public read access to the video files, so that we can access them without a token (see ). For this example I’ve added no authentication to keep things simple, so we’ll allow public write access too, but this should be changed in a production app. So in Firebase Console, go to Storage -> Rules and change it to: comment service firebase.storage {
 match /b/{bucket}/o {
 match /{allPaths=**} {
 allow read;
 allow write;
 }
 }
} Stages in client side video processing This is the sequence of steps we’ll have to do for each video: Get raw video path from image_picker Get aspect ratioGenerate thumbnail using ffmpeg Encode raw video into HLS files Upload thumbnail jpg to Cloud Storage Upload encoded files to Cloud Storage Save video metadata to Cloud Firestore Let’s go over every step and see how to implement it. Encoding Provider We’ll create an EncodingProvider class that will encapsulate the encoding logic. The class will hold the flutter_ffmpeg instances needed. { FlutterFFmpeg _encoder = FlutterFFmpeg(); FlutterFFprobe _probe = FlutterFFprobe(); FlutterFFmpegConfig _config = FlutterFFmpegConfig();

...
} class EncodingProvider static final static final static final Generating thumbnail We’ll use the encoder to generate a thumbnail which we’ll save later to Cloud Storage. We’re telling FFmpeg to take one frame ( option) out of videoPath ( option) with size of ( option). We check the result code to ensure the operation finished successfully. -vframes -i width x height -s Future< > getThumb(videoPath, width, height) { (File(videoPath).existsSync()); outPath = ; arguments = static String async assert final String ' .jpg' $videoPath final '-y -i -vframes 1 -an -s x -ss 1 $videoPath ${width} ${height} $outPath ';
 
 final int rc = await _encoder.execute(arguments);
 assert(rc == 0);
 assert(File(outPath).existsSync());

 return outPath;
} Getting video length and aspect ratio We’ll use and calculate the aspect ratio (needed for the flutter video player) and get video length (needed to calculate encoding progress): FlutterFFprobe.getMediaInformation Future< < , >> getMediaInformation( path) { _probe.getMediaInformation(path);
} getAspectRatio( < , > info) { width = info[ ][ ][ ]; height = info[ ][ ][ ]; aspect = height / width; aspect;
} getDuration( < , > info) { info[ ];
} static Map dynamic dynamic String async return await static double Map dynamic dynamic final int 'streams' 0 'width' final int 'streams' 0 'height' final double return static int Map dynamic dynamic return 'duration' Encoding Now for the actual video encoding. For this example I used the parameters from this excellent . We’re creating two variant streams, one with bitrate, and one with bitrate. This will generate multiple files (video chunks) for each variant quality stream, and one file (playlist) for each stream. It will also generate a that lists all the files. HLS tutorial 2000k 365k fileSequence.ts playlistVariant.m3u8 master.m3u8 playlistVariant.m3u8 Future< > encodeHLS(videoPath, outDirPath) { (File(videoPath).existsSync()); arguments = + + + + + + + + + + ; rc = _encoder.execute(arguments); (rc == ); outDirPath;
} static String async assert final '-y -i ' $videoPath '-preset ultrafast -g 48 -sc_threshold 0 ' '-map 0:0 -map 0:1 -map 0:0 -map 0:1 ' '-c✌0 libx264 -b✌0 2000k ' '-c✌1 libx264 -b✌1 365k ' '-c:a copy ' '-var_stream_map "v:0,a:0 v:1,a:1" ' '-master_pl_name master.m3u8 ' '-f hls -hls_time 6 -hls_list_size 0 ' '-hls_segment_filename " /%v_fileSequence_%d.ts" ' $outDirPath ' /%v_playlistVariant.m3u8' $outDirPath final int await assert 0 return Note: This is a simple encoding example, but the options are endless. For a complete list: https://ffmpeg.org/ffmpeg-formats.html Showing encoding progress Encoding can take a long time, and it’s important to show the user that something is happening. We’ll use FFmpeg’s to get the current encoded frame’s time, and divide by video duration to get the progress. We’ll then update the state field, which is connected to a . enableStatisticsCallback _progress LinearProgressBar { _progress = ;
 ... initState() {
 EncodingProvider.enableStatisticsCallback(( time, size, bitrate, speed, videoFrameNumber, videoQuality, videoFps) { (_canceled) ;

 setState(() {
 _progress = time / _videoDuration;
 });
 });

 ... .initState();
 }

 _getProgressBar() { Container(
 padding: EdgeInsets.all( ),
 child: Column(
 crossAxisAlignment: CrossAxisAlignment.center,
 mainAxisAlignment: MainAxisAlignment.center,
 children: <Widget>[
 Container(
 margin: EdgeInsets.only(bottom: ),
 child: Text(_processPhase),
 ),
 LinearProgressIndicator(
 value: _progress,
 ),
 ],
 ),
 );
 } < > class _MyHomePageState extends State MyHomePage double 0.0 @override void int int double double int double double if return super return 30.0 30.0 Uploading the files Now that encoding is done, we need to upload the files to Cloud Storage. Uploading a single file to Cloud Storage Uploading to Cloud Storage is quite straightforward. We get a into the path where we want the file to be stored with . Then we call , and listen to the event stream with _onUploadProgress, where we update the state field like we did with the encoding. When the uploading is done, the will return the url we can use to access the file. StorageReference FirebaseStorage.instance.ref().child(folderName).child(fileName) ref.putFile(file) _progress await taskSnapshot.ref.getDownloadURL() Future< > _uploadFile(filePath, folderName) { file = File(filePath); basename = p.basename(filePath); StorageReference ref =
 FirebaseStorage.instance.ref().child(folderName).child(basename);
 StorageUploadTask uploadTask = ref.putFile(file);
 uploadTask.events.listen(_onUploadProgress);
 StorageTaskSnapshot taskSnapshot = uploadTask.onComplete; videoUrl = taskSnapshot.ref.getDownloadURL(); videoUrl;
} _onUploadProgress(event) { (event.type == StorageTaskEventType.progress) { progress =
 event.snapshot.bytesTransferred / event.snapshot.totalByteCount;
 setState(() {
 _progress = progress;
 });
 }
} String async final new final final await String await return void if final double Fixing the HLS files Now we need to go over all the generated HLS file ( and ), and upload them into the Cloud Storage folder. But before we do, we need to fix them so that they point to the correct urls relative to their place in Cloud Storage. This is how the files are created on the client: .ts .m3u8 .m3u8 #EXTM3U
#EXT-X-VERSION: #EXT-X-TARGETDURATION: #EXT-X-MEDIA-SEQUENCE: #EXTINF: , _fileSequence_0.ts
#EXT-X-ENDLIST 3 3 0 2.760000 1 Notice the line . This is the relative path to the chunk in the playlist. But when we upload this to a folder, it’s missing the folder name from the URL. It’s also missing the query parameter, that’s required to get the actual file from Firebase, and not just the metadata. This is how it should look like: 1_fileSequence_0.ts .ts ?alt=media #EXTM3U
#EXT-X-VERSION: #EXT-X-TARGETDURATION: #EXT-X-MEDIA-SEQUENCE: #EXTINF: ,
video4494% F1_fileSequence_0.ts?alt=media
#EXT-X-ENDLIST 3 3 0 2.760000 2 So we need a function for adding these two things to each entry, and also to each entry in the master playlist: .ts .m3u8 _updatePlaylistUrls(File file, videoName) { lines = file.readAsLinesSync(); updatedLines = < >(); ( line lines) { updatedLine = line; (line.contains( ) || line.contains( )) {
 updatedLine = ;
 }
 updatedLines.add(updatedLine);
 } updatedContents = updatedLines.reduce((value, element) => value + + element);

 file.writeAsStringSync(updatedContents);
} void String final var List String for final String in var if '.ts' '.m3u8' ' %2F ?alt=media' $videoName $line final '\n' Uploading the HLS files Finally we’ll go through all the generated files and upload them, fixing the files as necessary: .m3u8 Future< > _uploadHLSFiles(dirPath, videoName) { videosDir = Directory(dirPath); playlistUrl = ; files = videosDir.listSync(); i = ; (FileSystemEntity file files) { fileName = p.basename(file.path); fileExtension = getFileExtension(fileName); (fileExtension == ) _updatePlaylistUrls(file, videoName);

 setState(() {
 _processPhase = ;
 _progress = ;
 }); downloadUrl = _uploadFile(file.path, videoName); (fileName == ) {
 playlistUrl = downloadUrl;
 }
 i++;
 } playlistUrl;
} String async final var '' final int 1 for in final final if 'm3u8' 'Uploading video file out of ' $i ${files.length} 0.0 final await if 'master.m3u8' return This is how the uploaded files look like in Cloud Storage: Saving metadata to Firestore After we have the video’s storage url, we can save the metadata in Firestore, allowing us to share the videos instantly between users. As we saw in the previous post, saving the metadata to Firestore is easy: Firestore.instance.collection( ). ().setData({ : video.videoUrl, : video.thumbUrl, : video.coverUrl, : video.aspectRatio, : video.uploadedAt, : video.videoName,
}); await 'videos' document 'videoUrl' 'thumbUrl' 'coverUrl' 'aspectRatio' 'uploadedAt' 'videoName' This is how the video document looks like in Firestore: Putting it all together Now putting it all together in a processing function that goes through all the stages we’ve seen and updates the state to display the current job status (the input comes from the output): rawVideoFile image_picker Future< > _processVideo(File rawVideoFile) { rand = ; videoName = ;
 _videoDuration = EncodingProvider.getDuration(info);
 _progress = ;
 }); thumbFilePath = EncodingProvider.getThumb(rawVideoPath, thumbWidth, thumbHeight);

 setState(() {
 _processPhase = ;
 _progress = ;
 }); encodedFilesDir = EncodingProvider.encodeHLS(rawVideoPath, outDirPath);

 setState(() {
 _processPhase = ;
 _progress = ;
 }); thumbUrl = _uploadFile(thumbFilePath, ); videoUrl = _uploadHLSFiles(encodedFilesDir, videoName); videoInfo = VideoInfo(
 videoUrl: videoUrl,
 thumbUrl: thumbUrl,
 coverUrl: thumbUrl,
 aspectRatio: aspectRatio,
 uploadedAt: .now().millisecondsSinceEpoch,
 videoName: videoName,
 );

 setState(() {
 _processPhase = ;
 _progress = ;
 }); FirebaseProvider.saveVideo(videoInfo);

 setState(() {
 _processPhase = ;
 _progress = ;
 _processing = ;
 });
 } void async final String ' ' ${ Random().nextInt( )} new 10000 final 'video /Videos/ Generating thumbnail' $rand ';
 final Directory extDir = await getApplicationDocumentsDirectory();
 final outDirPath = ' ${extDir.path} $videoName ';
 final videosDir = new Directory(outDirPath);
 videosDir.createSync(recursive: true);

 final rawVideoPath = rawVideoFile.path;
 final info = await EncodingProvider.getMediaInformation(rawVideoPath);
 final aspectRatio = EncodingProvider.getAspectRatio(info);

 setState(() {
 _processPhase = ' 0.0 final await 'Encoding video' 0.0 final await 'Uploading thumbnail to firebase storage' 0.0 final await 'thumbnail' final await final DateTime 'Saving video metadata to cloud firestore' 0.0 await '' 0.0 false Showing the video list We saw previously how to listen to Firestore and display a ListView of videos. In short, we used to listen to the update stream, and to create a list that reacts to changes in the stream, via the _videos state field. snapshots().listen() ListView.builder() For each video we display a containing a showing the video’s , and next to it the and field. I’ve used the plugin to display the upload time in a friendly way. Card fadeInImage.memoryNetwork thumbUrl videoName uploadedAt timeago _getListView() { ListView.builder(
 padding: EdgeInsets.all( ),
 itemCount: _videos.length,
 itemBuilder: (BuildContext context, index) { video = _videos[index]; GestureDetector(
 onTap: () {
 Navigator.push(
 context,
 MaterialPageRoute(
 builder: (context) { Player(
 video: video,
 );
 },
 ),
 );
 },
 child: Card(
 child: Container(
 padding: EdgeInsets.all( ),
 child: Stack(
 children: <Widget>[
 Row(
 crossAxisAlignment: CrossAxisAlignment.start,
 children: <Widget>[
 Stack(
 children: <Widget>[
 Container(
 width: thumbWidth.toDouble(),
 height: thumbHeight.toDouble(),
 child: Center(child: CircularProgressIndicator()),
 ),
 ClipRRect(
 borderRadius: BorderRadius.circular( ),
 child: FadeInImage.memoryNetwork(
 placeholder: kTransparentImage,
 image: video.thumbUrl,
 ),
 ),
 ],
 ),
 Expanded(
 child: Container(
 margin: EdgeInsets.only(left: ),
 child: Column(
 crossAxisAlignment: CrossAxisAlignment.start,
 mainAxisSize: MainAxisSize.max,
 children: <Widget>[
 Text( ),
 Container(
 margin: EdgeInsets.only(top: ),
 child: Text( ),
 ),
 ],
 ),
 ),
 ),
 ],
 ),
 ],
 ),
 ),
 ),
 );
 });
 } return const 8 int final return return new new 10.0 new 8.0 new 20.0 " " ${video.videoName} new 12.0 'Uploaded ' ${timeago.format( DateTime.fromMillisecondsSinceEpoch(video.uploadedAt))} new And there we have it: Limitations Jank Video encoding is a CPU-intensive job. Because flutter is single-threaded, running ffmpeg encoding will cause jank (choppy UI) while it’s running. The solution is of course to offload the encoding to a background process. Regrettably, I haven’t found a way to do this easily with flutter_ffmpeg. If you have a working approach for long-running video encoding jobs in the background, please let me know! (To mitigate the jank effects, you can show an encoding progress bar, and not allow any other use of the UI until it’s done.) Premature termination Another problem with long encoding / uploading jobs, is that the OS can decide to close your app’s process when it’s minimized, before the job has completed. You have to manage the job status and restart / resume the job from where it stopped. Public access to stored videos HLS serving from Cloud Storage with this method requires public read access for all files. If you need authenticated access to the videos, you’ll have to find a way to dynamically update the .m3u8 playlist with the Firebase token each time the client downloads the file (because the token will be different). Caching The flutter video_player plugin doesn’t currently support caching. You can try to use which supports caching (I use it in my app), but I haven’t tested it with HLS. this fork To use it, add this in : pubspec.yaml video_player: git: url: git://github.com/syonip/plugins.git ref: a669b59 path: packages/video_player Wrap Up I think this is a good DIY approach to hosting videos, for two main reasons: Minimal amount of coding - there’s no server code to write or maintain. Very cheap - If you’re working on a side project and want to start with zero cost, you can. Firebase’s free plan has 1GB storage and 10 GB/month transfer limit, which is good to start with. Thanks for reading! As always, full source code can be found on . GitHub If you have any questions, please leave a comment!

Stories, News & Mentions about FANVIDEO

Fanvideo’s Stories on HackerNoon

Fanvideo's News & Mentions