Selective Compression of Latent Representations for Variable-Rate Image Encoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing deep-learning-based image compression models require multiple separate models trained for different target compression levels, leading to increased complexity and inefficiency, as they are not adaptable for variable bit rates without additional network modules or complexity overhead.
Innovation Solution
The Selective Compression of Representations (SCR) method uses a 3D binary mask to selectively compress latent representations, adapting to target quality levels through a single 1×1 convolutional layer and importance adjustment curves, enabling variable-rate compression with reduced overhead and decoding time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple separate models are trained for different target compression levels, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements a single image compression model that can operate at multiple target compression levels (e.g., different bit rates from 0.5 to 2.0 bpp) without requiring separate trained models. The model achieves this through adaptive quantization mechanisms and conditional computation paths that dynamically adjust compression parameters based on the desired output quality, making one model perform the function of multiple specialized models
Solution Approach 2:
The patent introduces dynamic adaptation mechanisms within the compression model that allow it to adjust its behavior based on the target compression level. This includes dynamic quantization step selection, adaptive entropy coding parameters, and conditional neural network path selection that changes computation based on the desired bit rate, enabling the single model to efficiently handle variable compression requirements
2Device complexity
If a single model is used for variable bit rates, then device complexity is reduced, but adaptability deteriorates
Solution Approach 1:
The patent employs parameter adaptation mechanisms that allow the single model to adjust its internal parameters dynamically based on the target compression level. This includes adaptive quantization parameters, variable entropy coding settings, and conditional computation paths that are selected based on the desired bit rate, enabling the model to adapt its behavior across different operating conditions without requiring multiple specialized models
3Adaptability or versatility
If additional network modules are added for variable rate support, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent segments the compression model into modular components with distinct functions: an encoder that generates latent representations, a hyper-encoder that models the distribution of these representations, and a decoder that reconstructs images. This segmentation allows each module to be optimized independently while working together to achieve variable rate compression, reducing the need for additional complex network modules
Data Source
AI summary
Disclosed herein are a method, apparatus, and storage medium for image encoding/decoding. Selective compression learning of latent representations for variable-rate image compression is used for the method, apparatus, and storage medium. A selective compression method that partially encodes latent representations in a completely generalized manner for deep-learning-based variable-rate image compression is disclosed in embodiments. The methods of the embodiments adaptively determine essential representation elements for compression at different target quality levels.