GAN-Based Video Compression Resolving Quality-Complexity Trade-offs
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Solution Overview
Problem
Current video compression techniques, particularly in high efficiency video coding (HEVC) and versatile video coding (VVC), face challenges in efficiently compressing video signals while maintaining visually pleasing decoded videos, especially during high-demand events, and have not effectively utilized generative adversarial networks (GANs) for residue-frame coding.
Innovation Solution
A GAN-based video coding system that compresses both reference and target frames, utilizing a motion-compensated prediction module and a GAN-based residue-frame coding module to reduce bit rates while preserving perceptual quality, by training a GAN to encode and decode frames in the latent space and incorporating adversarial learning for improved detail preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If GAN-based compression is applied to video frames, then perceptual quality and texture detail are improved, but computational complexity and training difficulty increase
Solution Approach 1:
The video compression system is segmented into distinct functional modules: motion-compensated prediction module, residue computation module, and GAN-based residue coding module. This segmentation allows each module to be optimized independently, reducing overall computational complexity while maintaining perceptual quality improvements from GANs.
Solution Approach 2:
Motion-compensated prediction is performed preliminarily to generate accurate predictions before residue computation. This preliminary action reduces the magnitude and complexity of the residue that needs to be encoded by the GAN, thereby reducing the computational burden on the GAN while preserving perceptual quality.
2Manufacturing precision
If GANs are used for residue-frame coding, then visual quality is improved, but bandwidth requirements increase
Solution Approach 1:
The motion information and predictable frame components are extracted and encoded separately using traditional motion-compensated prediction. Only the residual difference that contains the essential visual quality information is passed to the GAN for encoding, reducing the amount of data that needs to be transmitted while maintaining visual quality.
Solution Approach 2:
The system applies different encoding strategies to different parts of the video signal: traditional motion-compensated prediction for the predictable majority of frame content, and GAN-based encoding only for the residue portion that requires high perceptual quality. This local differentiation reduces overall bandwidth requirements.
3Productivity
If CNN-based compression with MSE loss is used, then computational efficiency is maintained, but decoded images appear blurry and lack details
Solution Approach 1:
The GAN architecture introduces a discriminator as an intermediary component that provides adversarial feedback to the generator. This intermediary mechanism guides the generator to produce sharper, more detailed images without requiring a complete redesign of the compression system, maintaining computational efficiency while improving image quality.
Solution Approach 2:
The loss function is changed from simple MSE to a composite loss function that includes adversarial loss from the discriminator. This parameter change in the optimization objective transforms the generator's behavior to prioritize perceptual quality and sharpness over mere pixel accuracy, resolving the blur issue while maintaining reasonable computational efficiency.
Data Source
AI summary
A method and an apparatus for video processing are provided. The method includes that a decoding terminal receives a plurality of coded video frames coded using one or more generative adversarial networks (GANs), receives network parameters related to the one or more GANs, and decodes the plurality of coded video frames using GANs based on the network parameters. Further, the one or more GANs respectively implement one or more video coding functions including reference-frame coding, motion-compensated frame prediction, and residue-frame coding.