Image Block Quantization Parameter Selection for Video Decoding
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Solution Overview
Problem
Existing video compression technologies face challenges in achieving effective coding and decoding due to the loss of details in image blocks with different features during downsampling, leading to poor reconstructed image quality.
Innovation Solution
The method involves parsing a code stream to determine appropriate quantization parameters for image blocks based on their coding schemes, allowing for differential coding and reconstruction that accounts for the unique features of each block, thereby improving the coding and decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If downsampling coding scheme is used to compress image data, then compression efficiency is improved, but image quality deteriorates due to loss of details
Solution Approach 1:
The image is divided into multiple image blocks, and each block is independently classified into different categories (first category for smooth regions, second category for detailed regions). This segmentation allows different coding schemes to be applied to different regions, achieving both compression efficiency and image quality preservation.
Solution Approach 2:
Different quantization parameters are applied to different image blocks based on their category. Smooth regions use one quantization parameter while detailed regions use another, ensuring that each region is processed with the appropriate quality level. This local differentiation resolves the contradiction by maintaining high quality where needed while achieving compression in suitable areas.
2Device complexity
If uniform quantization parameter is applied to all image blocks, then device complexity is reduced, but coding effectiveness deteriorates
Solution Approach 1:
The quantization parameter is made dynamic rather than static. The system dynamically selects different quantization parameters based on the classification results of image blocks. This dynamic adaptation improves coding effectiveness without significantly increasing device complexity, as the selection process follows clear classification rules.
Solution Approach 2:
Image blocks are classified into different categories before the actual coding process. This preliminary classification allows the system to prepare appropriate quantization parameters in advance, making the subsequent coding process more effective while keeping the overall complexity manageable through systematic organization.
Data Source
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AI summary
This application provides an image processing method, device, and system. The method includes: parsing a code stream to obtain a first quantization parameter of a current image area in an original resolution coding scheme, a second quantization parameter of the current image area in a downsampling coding scheme, a coding scheme of a current to-be-reconstructed image block in the current image area, a quantization coefficient of the current to-be-reconstructed image block, and a prediction signal of the current to-be-reconstructed image block, where the coding scheme of the current to-be-reconstructed image block is the original resolution coding scheme or the downsampling coding scheme, and the current to-be-reconstructed image block is a part of the current image area; determining a quantization parameter of the current to-be-reconstructed image block according to the coding scheme of the current to-be-reconstructed image block; and reconstructing the current to-be-reconstructed image block based on the quantization parameter, the quantization coefficient, and the prediction signal of the current to-be-reconstructed image block, to obtain a current reconstructed image block. Therefore, a reconstructed image block obtained by a decoder side has a better effect.