What is Video Compression?
AI video compression is a method that reduces the size of video files without significantly lowering their quality by employing algorithms from artificial intelligence. This is accomplished by analyzing the video content and selectively reducing the amount of data required to represent it, all while preserving crucial visual details and reducing distortion in perception.
Conventional video pressure calculations, for example, the famous H.264 and H.265 guidelines, utilize fixed encoding and disentangling techniques that apply a bunch of predefined rules to pack video information. AI video compression algorithms, on the other hand, use machine learning to adaptively optimize the compression process for each video, resulting in higher compression ratios and better video quality.
AI video compression is especially useful in applications like streaming video, video conferencing, and mobile devices where bandwidth and storage space are limited. AI video compression can help reduce bandwidth costs, enhance streaming performance, and expand the amount of video content that can be stored on a device by reducing the size of video files.
There are currently a number of AI video compression technologies and tools on the market, including Google’s VP9 and AV1 codecs and Amazon, Facebook, and Netflix’s proprietary solutions.
Types of Video Compression
There are various AI video compression methods that use a variety of strategies to shrink video files. Some of the most typical kinds are as follows:
Compression based on Deep Learning: Convolutional neural networks (CNNs) and other deep learning algorithms are used in this strategy to optimize data compression and learn the spatial and temporal characteristics of video content. H.266/VVC, Google’s VP9, and Baidu’s ERNIE-ViD are three examples of deep learning-based compression techniques. This kind of pressure method considers the human visual framework’s qualities to decrease how much information expected to address a video. By eliminating less significant subtleties, for example, those that the natural eye is less delicate to, perceptual video coding can accomplish higher pressure proportions with negligible perceptual quality misfortune. Instances of perceptual video coding incorporate MPEG-4 Section 2 and H.264.
Coding that Predicts: Predicting the next frame based on previous frames is the goal of this method, which makes use of predictive algorithms to analyze video content. By encoding just the distinctions between outlines, prescient coding can accomplish high pressure proportions while keeping up with high video quality. H.264 and H.265/HEVC are two examples of predictive coding techniques.
Coding based on transforms: This kind of pressure procedure utilizes numerical changes, for example, discrete cosine change (DCT) or discrete wavelet change (DWT), to change over the video signal into recurrence space coefficients. Transform-based coding can reduce the amount of data required to represent the video while minimizing quality loss by applying quantization to these coefficients. MPEG-2, MPEG-4, and H.264 are examples of transform-based coding techniques.
The choice of AI video compression method is determined by the application’s requirements and constraints, such as bandwidth, storage capacity, and video quality. Each type of AI video compression has advantages and disadvantages.
Benefits of Video Compression
AI video compression offers several benefits, including:
Reduced File Sizes: AI video compression algorithms can significantly reduce the size of video files without compromising the visual quality of the video. This can lead to significant savings in storage and bandwidth costs, especially in applications that require the streaming or sharing of large video files.
Improved Video Quality: AI video compression algorithms can improve the visual quality of video by selectively reducing the amount of data needed to represent the video. This can result in better color accuracy, contrast, and sharpness, as well as reduced artifacts and noise in the video.
Faster Encoding and Decoding: AI video compression algorithms can encode and decode video data more quickly than traditional compression methods, thanks to their adaptive optimization and machine learning capabilities.
Increased Compatibility: AI video compression algorithms can be designed to work with a wide range of video codecs, players, and devices, making it easier to distribute and share video content across different platforms and networks.
Reduced Bandwidth Requirements: By reducing the size of video files, AI video compression can help reduce the amount of bandwidth required to stream or transfer video content, leading to faster and smoother playback.
Overall, AI video compression can help improve the efficiency and quality of video streaming, sharing, and storage, making it a valuable technology for a wide range of applications, from video conferencing and broadcasting to surveillance and entertainment.
Tools use for video compression
Video compression makes use of a number of AI tools, including:
x265: For encoding video streams into the H.265/HEVC video compression format, the open-source software library x265 is used. It utilizes a few simulated intelligence based methods, for example, between outline expectation, versatile quantization, and rate control, to accomplish high pressure proportions while keeping up with high video quality.
AV1: AV1 is a sovereignty free video codec created by the Partnership for Open Media (AOMedia) that utilizations progressed man-made intelligence calculations to accomplish high pressure effectiveness. Video data is compressed and file sizes are reduced by utilizing a number of methods, including adaptive quantization, spatial-temporal redundancy reduction, and inter-frame prediction.
Perseus from V-Nova: V-Nova PERSEUS is a video compression technology that achieves high compression ratios with minimal quality loss by utilizing AI-based strategies like predictive coding and perceptual quantization. It can be incorporated into existing video workflows and is designed to work with a variety of video codecs, including H.264 and H.265.
The Google VP9: Google created the open-source video compression format known as VP9, which makes use of a number of AI-based methods like intra-frame prediction and adaptive entropy coding to achieve high compression ratios while maintaining high video quality. Google services like YouTube and Google Meet make use of it.
Bitmovin: Bitmovin is a cloud-based video foundation stage that offers simulated intelligence controlled video encoding and transcoding administrations. Its AI-based compression algorithms can optimize the encoding parameters and analyze the video content in order to achieve high compression ratios and minimize the size of video files.
These are just a few examples of AI video compression tools. The tool of choice is determined by the application’s particular requirements and constraints, such as bandwidth, storage capacity, and video quality.
The future of video compression is likely to be shaped by ongoing advancements in AI and machine learning technologies, which are driving improvements in compression efficiency, video quality, and encoding speed. Some of the trends that are likely to shape the future of video compression include:
More Efficient Codecs: With the development of AI-based video compression techniques, we can expect to see more efficient video codecs that can achieve higher compression ratios with less data loss. These codecs will use machine learning algorithms to optimize video encoding parameters and reduce the overall bitrate of video streams.
Improved Quality: The increasing use of AI in video compression is expected to lead to significant improvements in video quality, including better color accuracy, contrast, and sharpness. AI algorithms will be used to selectively reduce the amount of data needed to represent the video, resulting in higher-quality video with fewer artifacts.
Faster Encoding and Decoding: With the development of AI-based compression algorithms, video encoding and decoding speeds are likely to increase significantly. This will allow for faster and more efficient video streaming, especially in applications that require real-time video delivery.
Enhanced User Experience: As video compression technologies continue to improve, we can expect to see enhanced user experiences with video content. Videos will load faster, buffer less frequently, and be more accessible on a wider range of devices.
More Efficient Video Streaming: With the increasing popularity of streaming video services, we can expect to see more efficient video streaming technologies that use AI-based compression techniques to reduce bandwidth requirements and improve video quality.
