Adaptive Quantization Parameter Adjustment for Image Compression
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Solution Overview
Problem
Current image processing techniques face challenges in efficiently encoding and decoding images, particularly in adapting quantization parameters based on image content, which affects compression efficiency and quality.
Innovation Solution
A method and system that access and adjust quantization parameters using implicit rate control, where a quantization parameter is determined for encoding a sample, and if a flatness criterion is met, it is adjusted and explicitly indicated to the decoding system, allowing for adaptive encoding and decoding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If adaptive quantization is applied based on image content, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies different quantization parameters to different regions of the image based on local flatness characteristics. Smooth regions use coarser quantization while detailed regions use finer quantization, optimizing compression efficiency for each region's specific content requirements.
Solution Approach 2:
The system uses decoder-side computation to derive motion vectors and flatness indicators without requiring additional encoder complexity. The decoder performs template matching and flatness analysis using already-decoded pixel data, making the adaptive quantization self-sufficient at the decoding stage.
2Measurement precision
If explicit quantization parameter transmission is used, then decoding accuracy is improved, but bandwidth usage increases
Solution Approach 1:
The patent extracts only the essential flatness indication information from the image content and transmits this compact representation to the decoder. Instead of transmitting full quantization parameter maps, only the derived flatness indicators are sent, significantly reducing bandwidth requirements while maintaining decoding accuracy.
Solution Approach 2:
The system transforms the image content into a different parameter space by computing flatness indicators from pixel values. This parameter transformation allows the decoder to derive appropriate quantization parameters from the compact flatness information without transmitting the full parameter set.
3Productivity
If decoder-side motion vector derivation is used, then coding efficiency is improved, but computational load increases
Solution Approach 1:
The patent performs template matching and flatness analysis only on a subset of pixels rather than the entire image. By processing only the necessary regions for motion vector derivation and quantization parameter selection, the computational load is reduced while maintaining coding efficiency improvements.
Data Source
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AI summary
A system adjusts the quantization parameter used to encode an image sample (e.g., pixel component). The system accesses a quantization parameter for encoding a current image pixel from an implicit rate control and obtains a measure of another image sample within a particular distance from the sample. When the measure meets a predetermined criterion, the system adjusts the quantization parameter, encodes the pixel after adjusting the quantization parameter, and explicitly indicates the adjusting of the quantization parameter to a decoding system.