Adaptive Entropy Model Selection for Video Compression
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Solution Overview
Problem
Existing video compression systems face challenges in reducing the amortization gap, which refers to the difference between the optimal entropy model for a specific instance and the model learned during training, leading to inefficiencies in encoding and decoding processes.
Innovation Solution
The proposed solution involves a video decoder and encoder that dynamically determine whether to use an updated entropy model or a prior entropy model based on an entropy model indication in the video data. This allows for adaptive selection of the most appropriate entropy model for each picture, optimizing compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If an updated entropy model is used for decoding the current picture, then compression efficiency is improved, but the complexity of the decoding process increases
Solution Approach 1:
The patent applies dynamics by making the entropy model selection adaptive rather than fixed. The decoder dynamically chooses between using an updated entropy model or a prior entropy model based on the entropy model indication signal in the bitstream, allowing the system to adjust its complexity based on the specific encoding conditions and achieve optimal compression efficiency without unnecessary computational overhead
Solution Approach 2:
The patent changes the parameter of entropy model selection from a static configuration to a dynamic parameter that can be modified during decoding. By introducing the entropy model indication as a controllable parameter in the bitstream, the system can switch between different entropy models (updated vs. prior) to optimize the balance between compression efficiency and decoding complexity for each specific picture
2Productivity
If adaptive entropy model selection is implemented, then compression efficiency is improved, but the overhead for indicating the selected model increases
Solution Approach 1:
The patent applies local quality by making the entropy model indication specific to each picture or coding unit rather than applying a uniform model throughout. The entropy model indication signal is locally inserted in the bitstream at the picture or coding unit level, allowing the system to optimize compression efficiency for each local region while minimizing the overhead by only signaling changes where necessary
3Productivity
If the updated entropy model is always used, then compression efficiency is maximized, but the reliability of the decoding process decreases due to potential model instability
Solution Approach 1:
The patent implements feedback by using the entropy model indication signal as a control mechanism that provides information about which entropy model was used during encoding. This feedback allows the decoder to reliably reproduce the encoding process by selecting the corresponding entropy model, ensuring decoding reliability while still benefiting from the improved compression efficiency of updated models when appropriate
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed herein for reducing the amortization gap in end-to-end image compression and/or video compression. In examples, a video decoder may obtain an entropy model indication in video data. Based on the entropy model indication, the decoder may determine an entropy model to use for decoding a current picture. The current picture may be decoded based on the determined entropy model. In examples, the entropy model indication may indicate whether to use an updated entropy model or a prior entropy model for decoding the current picture. In examples, the entropy model indication may indicate an updated entropy model or a learned entropy model to use for decoding the current picture.