Latent Channel Stability Checks for Error-Resistant Image Compression
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Solution Overview
Problem
The reliability and stability of learnable image compression models are not adequately ensured due to insufficient theoretical analysis, leading to potential corruption in reconstructed images during continuous compression, and existing methods lack effective tools for corruption detection and error resistance.
Innovation Solution
An image compression error detection method that extracts a stability measurement region from a training image's latent representation, allowing for accurate detection of compression errors by comparing it with an admissible or in-range region, and an error-resistant method that adjusts corrupted channels using a stability constraint region to ensure stable reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If continuous image compression is performed using learnable image compression models, then compression efficiency and rate-distortion performance are improved, but image stability and reliability deteriorate due to potential corruption in reconstructed images
Solution Approach 1:
The patent pre-calculates stability measurement regions and admissible regions for latent representation channels during training. These regions are stored and used during inference to detect compression errors without requiring retraining or additional computational resources at runtime, enabling reliable continuous compression
Solution Approach 2:
The patent introduces a feedback mechanism that detects compression errors by comparing latent representation channels against pre-defined admissible regions. When errors are detected, the system can trigger retransmission or correction protocols, ensuring image stability throughout continuous compression processes
2Difficulty of detecting and measuring
If existing corruption detection methods are used, then some corrupted images can be identified, but detection accuracy is insufficient and manual threshold setting is required
Solution Approach 1:
The patent transforms the corruption detection problem into a parameter comparison problem by defining admissible regions for each latent representation channel based on training data statistics. During inference, actual channel values are compared against these pre-computed regions, eliminating the need for manual threshold setting and improving detection accuracy automatically
3Adaptability or versatility
If principal component analysis is applied to explain learnable image compression, then theoretical understanding is improved, but performance degrades in practical use due to non-linear transformations
Solution Approach 1:
The patent segments the latent representation into multiple channels and analyzes each channel's statistical properties independently during training. This segmentation allows the system to capture non-linear characteristics of each channel separately, providing accurate stability measurement without the performance degradation associated with global principal component analysis
Data Source
AI summary
An image compression error detection method, comprising: acquiring a training image data set; extracting a first latent representation of the training image by an image compression model, wherein the first latent representation is a multi-channel latent representation for image compression; processing the first latent representation to obtain a stability measurement region for detecting an image compression error; and extracting a first latent representation of a test image, and comparing the first latent representation of the test image with the stability measurement region to obtain an image compression error detection result. This method can efficiently detect the error and corruption caused by the neural network-based image compression, and efficiently realizes stable continuous image compression, which is applicable to the actual image communication scene.