Image Quantization Control for Area-Specific Quality Loss
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Solution Overview
Problem
Existing image compression methods apply uniform quantization strategies across the entire image, leading to suboptimal quality loss and artifacts, as they do not account for the varying content within different areas of an image.
Innovation Solution
Implement a sequence of quality control metrics tailored to specific areas of an image, iteratively processing transform coefficients based on an error level definition to minimize bit cost and quality loss, allowing for a non-linear quality loss curve.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If uniform quantization strategy is applied across the entire image, then the compression process is simple and fast, but the quality loss is suboptimal and artifacts appear due to not accounting for varying content in different areas
Solution Approach 1:
The image is divided into multiple areas or regions, each processed with its own quantization strategy. Different areas of the image are identified and processed separately, allowing tailored quantization approaches for different content types (e.g., smooth regions vs. detailed regions), thereby improving quality loss control while maintaining reasonable processing complexity.
Solution Approach 2:
Different quantization strategies are applied to different areas of the image based on local content characteristics. High-quality quantization is applied to important areas while lower-quality quantization is applied to less critical areas, optimizing overall quality loss control without uniformly processing the entire image.
2Manufacturing precision
If area-specific quality control metrics are implemented, then quality loss is minimized and artifacts are reduced, but the compression process becomes more complex
Solution Approach 1:
The image is preprocessed to identify different areas and determine appropriate quality control metrics before the actual quantization process. This preliminary analysis allows the main compression process to proceed more efficiently with pre-determined parameters, reducing the complexity burden during the actual compression phase.
Solution Approach 2:
The quantization process uses dynamic error level definitions that can be adjusted based on the specific area being processed. The system adaptively selects and applies different quality control metrics dynamically throughout the compression process, allowing flexibility while managing complexity through systematic adaptation rather than rigid uniform processing.
Data Source
AI summary
The loss of image quality during compression is controlled using a sequence of quality control metrics. The sequence of quality control metrics is selected for quantizing transform coefficients within an area of the image based on an error level definition. Candidate bit costs are then determined by quantizing the transform coefficients according to the error level definition or a modified error level and the sequence of quality control metrics. Where the candidate bit cost resulting from using the modified error level is lower than the candidate bit cost resulting from using the error level definition, the transform coefficients are quantized according to the modified error level and the sequence of quality control metrics. Otherwise, the transform coefficients are quantized based on the error level definition and according to the sequence of quality control metrics.