Neural Video Decoder Adaptation Using Side Information
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Solution Overview
Problem
Existing image and video compression methods based on neural networks lack adaptability and efficiency, particularly in handling diverse content and targeting multiple bitrates, as they rely on fixed decoders that do not account for varying content characteristics.
Innovation Solution
Implementing a method and apparatus that utilize additional side-information to parametrize neural-network decoders, using a second encoder to learn and send side information alongside the bitstream, allowing the decoder to adapt to specific content characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed neural network decoder is used for compression, then the device complexity is reduced, but the adaptability to diverse content and bitrates deteriorates
Solution Approach 1:
The decoder is transformed from a fixed static model to a dynamic adaptive model that can adjust its parameters based on content characteristics. The adaptation mechanism allows the decoder to modify its internal parameters during operation to match different content types and bitrate requirements, resolving the contradiction between adaptability and complexity by making complexity conditional rather than fixed.
Solution Approach 2:
The patent introduces parameter adaptation where the decoder's internal parameters are changed based on content characteristics. By modifying parameters such as filtering strength, resolution levels, or processing modes according to the specific content being decoded, the system achieves high adaptability while maintaining a base decoder architecture that prevents excessive complexity increase.
2Productivity
If additional side-information is added to the bitstream for decoder adaptation, then the compression efficiency is improved, but the bitstream size increases
Solution Approach 1:
The most critical content characteristics are extracted and encoded as side-information only when necessary. Rather than transmitting all possible metadata, the system identifies and extracts only the essential parameters needed for decoder adaptation, such as content type indicators or bitrate hints, thereby minimizing the additional bitstream size while maintaining compression efficiency improvements.
Solution Approach 2:
The system applies partial adaptation by adding side-information only for specific content types or conditions rather than universally for all content. This selective approach allows the system to improve compression efficiency for targeted content while avoiding the overhead of excessive side-information for content that can be handled with default decoder parameters.
3Adaptability or versatility
If a second encoder is added to generate side-information, then the adaptability of the decoding system is improved, but the device complexity increases
Solution Approach 1:
The second encoder is designed with multi-functionality to generate side-information for multiple content types and scenarios using a unified architecture. By creating a universal encoder that can handle various content characteristics through a single model rather than multiple specialized encoders, the system achieves high adaptability while controlling the increase in device complexity through shared computational resources.
Data Source
AI summary
Methods and apparatuses for encoding/decoding an image or a video using neural network are disclosed. In some embodiments, side-information is decoded from a bitstream that allows for adapting a first neural network-based decoder, the decoded side-information and coded data representative of an image or a video obtained from the bitstream or a separate bitstream are provided as inputs to the first neural-network-based decoder and a reconstructed image or video is obtained from an output of the first neural network-based decoder.