Image Coding With Selective High-Frequency Quantization
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Solution Overview
Problem
The existing High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) methods face challenges in efficiently reducing encoding amounts while maintaining image quality, particularly in handling orthogonal transform coefficients of high-frequency components.
Innovation Solution
An image coding apparatus that encodes images in blocks of P×Q pixels, orthogonally transforms prediction residuals, and quantizes using N×M arrays of quantization matrices to generate quantized coefficients, with specific techniques for zeroing out high-frequency components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If orthogonal transform coefficients of high-frequency components are reduced through quantization, then compression efficiency is improved, but image quality deteriorates
Solution Approach 1:
The patent applies different quantization strategies to different frequency components. High-frequency orthogonal transform coefficients are subjected to aggressive quantization (zeroing out) to maximize compression, while low-frequency coefficients retain more precision to maintain image quality. This local differentiation resolves the contradiction by optimizing each component according to its importance.
Solution Approach 2:
The patent changes the quantization parameter dynamically based on block size and frequency content. For larger blocks with more high-frequency coefficients, stronger quantization is applied. The quantization step size and threshold values are adjusted as parameters to balance compression ratio and quality preservation, resolving the efficiency-quality tradeoff.
2Productivity
If larger block sizes are used for orthogonal transform, then coding efficiency is improved, but encoding complexity increases
Solution Approach 1:
The patent segments the block into frequency bands or zones, applying different processing rules to different segments. Instead of uniformly processing all coefficients in a large block, the system divides the frequency spectrum and applies optimized quantization thresholds to each segment, reducing overall computational complexity while maintaining efficiency gains from large blocks.
Solution Approach 2:
The patent applies partial zeroing out rather than complete quantization. Instead of processing all high-frequency coefficients equally, the system selectively zeros out only the most significant ones beyond a certain threshold, reducing calculation amount while preserving the essential compression benefit of large block processing.
3Productivity
If more orthogonal transform coefficients are processed, then compression ratio is improved, but calculation amount increases
Solution Approach 1:
The patent extracts and processes only the most significant high-frequency coefficients that contribute most to compression. By identifying and zeroing out coefficients below certain thresholds, the system removes unnecessary computational work while maintaining the compression ratio benefit of processing many coefficients.
Solution Approach 2:
The patent skips the detailed processing of insignificant high-frequency coefficients. Instead of uniformly processing all coefficients, the system rapidly zeros out coefficients that fall below threshold values, skipping the computationally intensive quantization process for these less important components while maintaining effective compression.
Data Source
AI summary
An orthogonal transform unit orthogonally transforms prediction residuals in a block of a P×Q array of pixels, thereby generating an N×M (N is an integer satisfying N<P, and M is an integer satisfying M<Q) array of orthogonal transform coefficients. A quantization unit quantizes the N×M array of the orthogonal transform coefficients using at least a quantization matrix of an N×M array of elements, and thereby generates an N×M array of quantized coefficients.