Encoder Quantization Matrix Subset Selection for Coding Efficiency
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Solution Overview
Problem
Existing video coding technologies face challenges in improving coding efficiency, enhancing image quality, and reducing circuit scale while managing processing resources effectively.
Innovation Solution
The proposed solution involves an encoder that generates a first quantization matrix and selects a subset of matrix elements based on threshold values for transform coefficients, optimizing the quantization process to improve coding efficiency and reduce resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a full quantization matrix is used for all transform coefficients, then quantization accuracy is maintained, but processing complexity and resource usage increase
Solution Approach 1:
The quantization process is segmented by dividing transform coefficients into frequency bands (low-frequency and high-frequency regions) and applying different quantization matrices to each segment. This allows the system to maintain high quantization accuracy for important low-frequency coefficients while using simplified processing for less critical high-frequency coefficients, thereby resolving the contradiction between quantization accuracy and processing complexity.
Solution Approach 2:
Different quantization strategies are applied to different regions of the frequency spectrum based on their importance. Low-frequency coefficients, which contain most of the image information, receive full-precision quantization treatment, while high-frequency coefficients use reduced-precision quantization. This local differentiation maintains overall quantization accuracy while reducing processing complexity in less critical areas.
2Manufacturing precision
If advanced filtering and motion vector selection are applied, then image quality improves, but processing resource usage increases
Solution Approach 1:
The system applies advanced filtering and motion vector selection selectively rather than uniformly across all video blocks. By identifying blocks that benefit most from these operations and applying them only to those cases, the system achieves high image quality where needed while minimizing processing resource usage overall, thus resolving the contradiction between image quality and processing resource consumption.
Data Source
AI summary
Various embodiments provide an encoder that generates a plurality of quantization matrix elements for a current block; generates a quantization matrix using the plurality of quantization matrix elements; and quantizes, using the quantization matrix, transform coefficients of the current block. The quantization matrix includes only a subset of quantization matrix elements of the plurality of quantization matrix elements. Each of the subset of quantization matrix elements has an x-coordinate value less than a threshold x-coordinate value, a y-coordinate value less than a threshold y-coordinate value, or an x-coordinate value less than the threshold x-coordinate value and a y-coordinate value less than the threshold y-coordinate value.