How can you optimize video compression and encoding with artificial intelligence and machine learning?

2 Why are video compression and encoding important?

Video compression and encoding are important for several reasons. First, they help you save storage space and bandwidth, which can lower your costs and improve your performance. Second, they help you ensure compatibility and accessibility, as different platforms and devices may require different video formats and resolutions. Third, they help you optimize your video quality, as different compression and encoding settings can affect the visual and audio aspects of your video, such as color, contrast, sharpness, and noise.

3 How can AI and ML optimize video compression and encoding?

Artificial intelligence (AI) and machine learning (ML) are technologies that can learn from data and perform tasks that normally require human intelligence and judgment. AI and ML can optimize video compression and encoding by using advanced algorithms and models that can analyze, process, and enhance video data more efficiently and effectively than traditional methods. Some of the ways that AI and ML can optimize video compression and encoding are:

4 AI-based codecs

AI-based codecs are codecs that use AI and ML to compress and encode video data, using techniques such as neural networks, deep learning, and generative adversarial networks. AI-based codecs can achieve higher compression ratios and better quality than conventional codecs, by learning from large datasets of video content and adapting to different scenarios and preferences. For example, some AI-based codecs can compress 4K videos to 10% of their original size, while maintaining or even improving their quality.

5 AI-based quality enhancement

AI-based quality enhancement is the process of using AI and ML to improve the quality of video data, either before or after compression and encoding. AI-based quality enhancement can use techniques such as super-resolution, noise reduction, color correction, and frame interpolation to enhance the visual and audio aspects of video data, such as resolution, clarity, brightness, and smoothness. For example, some AI-based quality enhancement tools can upscale low-resolution videos to high-resolution videos, or remove artifacts and distortions caused by compression and encoding.

6 AI-based adaptive streaming

AI-based adaptive streaming is the process of using AI and ML to adjust the compression and encoding settings of video data according to the network conditions and device capabilities of the viewers. AI-based adaptive streaming can use techniques such as bitrate adaptation, resolution adaptation, and format adaptation to deliver the best possible video quality and experience to the viewers, while minimizing buffering, lagging, and stuttering. For example, some AI-based adaptive streaming platforms can dynamically switch between different video formats and resolutions, depending on the bandwidth and screen size of the viewers.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?