Y-UV Feature Fusion in Image Decompression for Lower Compute
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Solution Overview
Problem
Existing image/video compression methods using convolutional neural networks face inefficiencies due to separate processing of Y and UV components, leading to high computational complexity and reduced performance.
Innovation Solution
An image decompression method that optimizes the fusion of Y and UV components by performing concatenation and convolution operations, allowing for flexible channel management to reduce computational load while enhancing performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If separate processing of Y and UV components is performed, then processing simplicity is maintained, but computational complexity increases and performance decreases
Solution Approach 1:
The patent merges the separate processing paths of Y and UV components into a unified processing framework. The encoder and decoder networks are integrated to perform joint optimization, where features from both components are processed together through shared convolutional layers and optimization objectives, reducing overall computational complexity while improving compression performance
2Device complexity
If separate processing of Y and UV components is performed, then processing simplicity is maintained, but performance decreases
Solution Approach 1:
The patent combines Y and UV component processing into an integrated neural network framework that performs joint optimization. The encoder network processes both components simultaneously with shared feature extraction, and the decoder reconstructs them through coordinated operations, achieving superior compression performance compared to separate processing
3Power
If channel counts are reduced during fusion, then computational power requirements decrease, but information loss may increase
Solution Approach 1:
The patent dynamically adjusts the number of channels in feature tensors during the fusion process. The convolutional layers transform feature tensors with different channel counts into unified representations, allowing flexible parameter changes that reduce computational load while preserving essential information through learned feature transformations
Data Source
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AI summary
This application provides an image decompression method and apparatus. The image decompression method in this application includes: obtaining a first feature tensor, where the first feature tensor corresponds to a first component of a reconstructed image; obtaining a second feature tensor, where the second feature tensor corresponds to a second component of the reconstructed image; performing concatenation and convolution on the first feature tensor and the second feature tensor to obtain a third feature tensor; obtaining a fourth feature tensor based on the third feature tensor and the second feature tensor; and obtaining the reconstructed image based on the first feature tensor and the fourth feature tensor. In embodiments of this application, a structure for fusing a Y component and a UV component is optimized. This can reduce a quantity of channels, to save computing power and implement lightweight. Even if the quantity of channels is not reduced, performance can still be greatly improved.