Variance-Based Quantization Parameter Determination for Video Encoding
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Solution Overview
Problem
Current variance adaptive quantization methods in video encoding fail to accurately determine quantization parameters for image blocks with gradient colors or brightness gradations, leading to higher compression rates and lower image quality in sensitive areas.
Innovation Solution
The method involves calculating a plane-based variance by subtracting an image moment from the general variance of image data blocks, which reflects the image gradient, to determine more appropriate quantization parameters that are closer to those of flat areas, thereby reducing distortion in gradient regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If variance adaptive quantization is applied to compress image blocks, then data quantities are reduced, but image quality deteriorates in gradient areas
Solution Approach 1:
The patent applies different quantization parameters to different regions of the image block based on their visual characteristics. By calculating the variance of the image block and comparing it against threshold values, the system identifies gradient areas versus flat areas and assigns appropriate QPs accordingly. This ensures that gradient areas receive higher QP values for compression while flat areas maintain lower QP values for quality preservation.
Solution Approach 2:
The patent dynamically adjusts the quantization parameter (QP) based on the calculated variance of each image block. By changing the QP parameter according to the variance threshold comparisons, the system optimizes the balance between compression efficiency and image quality. The QP is increased for blocks with higher variance (gradient areas) and decreased for blocks with lower variance (flat areas).
2Quantity of substance
If higher compression rates are applied to gradient areas, then data quantities are reduced, but distortion increases in these areas
Solution Approach 1:
The patent identifies gradient areas through variance calculation and applies higher quantization parameters specifically to these regions. This local adaptation allows the system to compress gradient areas more aggressively while preserving flat areas with lower QP values, thereby reducing overall data quantity without introducing excessive distortion in visually critical regions.
Solution Approach 2:
The patent replaces the traditional uniform compression approach with a variance-based adaptive system. By substituting the mechanical process of uniform quantization with a statistical approach using variance calculation and threshold comparison, the system achieves intelligent compression that adapts to the local characteristics of each image block.
Data Source
AI summary
A method of determining quantization parameters includes the steps of: receiving a block of image data; calculating a general variance of the block of image data; calculating a plane-based variance of the block of image data by subtracting an image moment of the block of image data from the general variance; and determining a quantization parameter for the block of image data according to the plane-based variance.