Predictive Quantization Coding for Video Compression
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Solution Overview
Problem
Existing predictive quantization coding methods face challenges in achieving high data compression ratios with minimal distortion loss, as they often misjudge prediction pixel components and fail to fully utilize texture correlation, leading to high computational complexity and limited entropy reduction.
Innovation Solution
A predictive quantization coding method that divides pixels into components, calculates texture direction gradients, and uses reference pixels to obtain prediction residuals, adaptively performing quantization to reduce bandwidth and entropy, while optimizing rate distortion through inverse quantization and compensation processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing predictive quantization coding methods are used, then the coding process is simple, but the prediction pixel components are easily misjudged and texture correlation is not fully utilized, leading to high distortion loss and limited compression ratio improvement
Solution Approach 1:
The pixel is divided into multiple pixel components (e.g., R, G, B components) for separate processing. Each pixel component is processed independently through the prediction and quantization steps, allowing more precise handling of each component's characteristics while maintaining overall system manageability
Solution Approach 2:
Different weighting gradients are applied to different pixel components based on their local texture characteristics. The method calculates texture direction gradients for each pixel component and applies adaptive weighting, ensuring that each local region is processed with appropriate precision tailored to its specific properties
2Device complexity
If existing predictive quantization coding methods are used, then the computational complexity is reduced, but the theoretical limit entropy cannot be further reduced and data compression ratio is limited
Solution Approach 1:
The quantization step size is dynamically adjusted based on the prediction residual characteristics and texture direction gradients. The method performs adaptive quantization where the quantization parameters are modified according to the local image properties, enabling more efficient entropy reduction while maintaining computational feasibility
Solution Approach 2:
The method changes multiple parameters including quantization step sizes, weighting gradient values, and prediction weights based on the analyzed texture characteristics. By adaptively adjusting these parameters according to the image content, the theoretical limit entropy is reduced more effectively without requiring excessive computational resources
3Ease of operation
If existing predictive quantization coding methods are used, then the coding process is straightforward, but distortion loss after compression cannot be further reduced
Solution Approach 1:
The method employs rate-distortion optimization where the quantization process incorporates feedback from the prediction residual analysis. The quantization step sizes are adjusted based on the measured distortion characteristics, creating a feedback loop that continuously optimizes the balance between compression efficiency and distortion reduction
Solution Approach 2:
The method performs preliminary analysis of texture direction gradients and positional relationships before the main quantization process. By pre-calculating the optimal prediction weights and identifying dominant texture directions in advance, the subsequent quantization step can be performed more effectively with reduced distortion
Data Source
AI summary
The present invention relates to a predictive quantization coding method and a video compression system. The method includes: dividing a pixel to be processed into a plurality of pixel components; obtaining one pixel component to be processed and texture direction gradients thereof; obtaining reference pixels and a prediction residual of the pixel component to be processed; forming a prediction residual code stream; dividing the prediction residual code stream into multiple quantization units; and obtaining a quantization residual code stream. The present invention can reduce the transmission bandwidth, and reduce the theoretical limit entropy and complexity.