Neural Latent Entropy Coding with Channel Reordering
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Solution Overview
Problem
Current end-to-end neural compression methods for images and videos lack standardized processes and fail to optimize latent entropy coding, leading to inefficiencies in redundancy reduction and compression performance.
Innovation Solution
The proposed method improves latent entropy coding by considering channel activity, performing post-conditional entropy coding based on context, reordering channels for inter-channel correlation, and signaling activity, while optimizing the main latent for a particular image, without requiring retraining of existing auto-encoders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If standard end-to-end neural compression is used, then the compression process is simple and automated, but latent entropy coding is not optimized and redundancies are not reduced effectively
Solution Approach 1:
The patent segments the latent representation into multiple channels and applies different coding strategies to each channel based on its activity level. Important channels are coded with higher precision while less important channels are coded with lower precision, optimizing the balance between compression efficiency and information preservation.
Solution Approach 2:
The patent applies local quality by differentiating the coding quality across different channels based on their importance. Channels with higher activity (more important for image quality) receive more bits for coding, while channels with lower activity receive fewer bits, creating non-uniform quality distribution that optimizes overall compression performance.
2Productivity
If channel activity indication is coded to improve compression, then redundancy is reduced, but encoding complexity increases
Solution Approach 1:
The patent applies partial action by coding channel activity indication only for channels that exceed a certain activity threshold. Instead of coding all channels uniformly, the system identifies and codes only the most important channels, reducing the overall encoding complexity while maintaining compression effectiveness.
3Productivity
If post-conditional entropy coding is performed, then encoding efficiency improves, but computational overhead increases
Solution Approach 1:
The patent applies preliminary action by pre-computing and storing probability models for different channel activity patterns during training. During actual encoding, the system simply looks up the appropriate probability model based on the observed channel activity, avoiding the need for complex real-time computations while still achieving improved encoding efficiency.
4Productivity
If channels are reordered to improve inter-channel correlation, then compression efficiency improves, but processing time increases
Solution Approach 1:
The patent applies preliminary action by pre-determining the optimal channel reordering through training on large datasets. The reordering strategy is encoded as part of the model parameters and applied directly during inference without requiring time-consuming computation, thus improving compression efficiency while minimizing processing time overhead.
Data Source
AI summary
In end-to-end compression, a deep neural-network based encoder can be used to encode an image. The embeddings output from the encoder are quantized and encoded with a lossless encoder. Advantageously, at least one embodiment allows improving the latent entropy coding by further reducing the redundancies in the quantized latent. To that end, at least one embodiment discloses taking into account channels importance by coding an indication of a channel activity (or significance): performing post-conditional entropy coding by computing conditional probability based on a context afterwards: using channels reordering to improve inter channel correlation: or performing RDOQ like process by optimizing the main latent for a particular image.