Image Encoding Device Partial Transform for Residual Quality
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Solution Overview
Problem
When the similarity between reference images is low, the prediction accuracy of image encoding devices decreases, leading to large prediction residuals and degradation of image quality, which affects encoding efficiency.
Innovation Solution
An image encoding device that evaluates the similarity between reference images on a pixel-by-pixel basis and applies orthogonal transform and quantization only to specific partial areas with high prediction residuals, improving energy compaction and reducing degradation of transform coefficients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If orthogonal transform and quantization are applied to the entire prediction residual, then encoding is performed uniformly across all areas, but quantization degradation propagates to areas with high prediction accuracy, lowering overall image quality
Solution Approach 1:
The prediction residual is divided into multiple regions based on prediction accuracy evaluation. High-accuracy regions and low-accuracy regions are segmented separately, allowing different processing strategies to be applied to each region, preventing quantization degradation from propagating to high-accuracy areas.
Solution Approach 2:
Different quantization strategies are applied to different regions: high-accuracy regions use skip processing or lower quantization strength to preserve quality, while low-accuracy regions undergo full orthogonal transform and quantization. This local differentiation optimizes overall image quality by protecting important regions.
2Loss of energy
If orthogonal transform is applied to prediction residuals with large values, then energy compaction is achieved, but high frequency components degrade due to rough quantization, and degradation propagates through inverse transform
Solution Approach 1:
Orthogonal transform and quantization are applied partially only to low-accuracy regions rather than the entire residual. This partial action maintains energy compaction benefits in regions where needed while avoiding excessive quantization degradation in high-accuracy regions.
3Adaptability or versatility
If prediction is performed using multiple reference images, then prediction coverage is improved, but areas with low similarity between reference images produce large prediction residuals, reducing encoding efficiency
Solution Approach 1:
The prediction accuracy evaluation is performed preliminarily before orthogonal transform and quantization. This preliminary evaluation identifies regions that will benefit from skip processing, allowing the encoding process to adaptively optimize efficiency while maintaining prediction coverage benefits.
Data Source
AI summary
An image encoding device (1) encodes a block-based target image. The image encoding device (1) comprises: a predictor (109) configured to generate a prediction image corresponding to the target image by performing prediction using a plurality of reference images; an evaluator (111) configured to evaluate a degree of similarity between the plurality of reference images on a pixel-by-pixel basis; a calculator (101) configured to calculate a prediction residual indicating a pixel-based difference between the target image and the prediction image; a determiner (112) configured to determine a partial area, to which an orthogonal transform and quantization are to be applied, of the prediction residual based on a result of the evaluation by the evaluator; and a transformer/quantizer (102) configured to perform an orthogonal transform and quantization only for the partial area in the prediction residual.