Image Coding Quantization Step Adjustment via Local Activity
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Solution Overview
Problem
Existing image coding systems, such as MPEG2 and H.264, face challenges in maintaining consistent quantization step values across macroblocks, leading to local variations that degrade subjective image quality and increase code amounts unnecessarily.
Innovation Solution
An image coding apparatus that calculates activity values for macroblocks, determines statistical values of surrounding macroblocks, and adjusts quantization step values using a correction factor based on these calculations to ensure consistent quantization across macroblocks, thereby reducing local variations and improving image quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the quantization step value is determined based on the macroblock evaluation value alone, then the coding efficiency is improved, but the subjective image quality deteriorates due to local variations
Solution Approach 1:
The patent applies local quality by determining the quantization step value based on both the macroblock evaluation value and the evaluation values of surrounding macroblocks. This ensures that each macroblock receives appropriate quantization treatment relative to its context, preventing excessive coarse quantization in low-activity areas while maintaining coding efficiency. The quantization step value is adjusted according to the local activity distribution, achieving a balance between compression efficiency and image quality.
2Quantity of substance
If the quantization step value is determined based on the macroblock evaluation value alone, then the code amount is reduced, but the image quality deteriorates due to unnecessary fine quantization
Solution Approach 1:
The patent prevents unnecessary fine quantization by considering the activity distribution of surrounding macroblocks when determining the quantization step value. In areas where surrounding macroblocks have low activity, the quantization step value is adjusted to avoid excessive fine quantization, thereby reducing code amount while maintaining adequate image quality. This contextual approach ensures that fine quantization is applied only where necessary.
3Productivity
If different quantization step values are assigned to different macroblocks, then the coding efficiency is improved, but the local variation in quantization causes image quality degradation
Solution Approach 1:
The patent resolves this contradiction by determining quantization step values that are locally adapted but contextually consistent. The quantization step value for each macroblock is adjusted based on the activity evaluation values of surrounding macroblocks, ensuring that quantization remains relatively uniform across similar regions while still allowing for efficient coding. This contextual constraint prevents excessive local variation in quantization step values.
Data Source
AI summary
A statistical value calculation part specifies macroblocks positioned around an object macroblock and calculates a minimum average value of activities of the macroblocks. When images of the macroblocks are flat and the minimum average value is smaller than an activity of the object macroblock, the minimum average value is set as an adjustment value. A correction factor determination part determines a correction factor on the basis of the adjustment value and a factor determination table. By multiplying a reference quantization step value by the correction factor, a quantization step value of the object macroblock is determined. Since the quantization step value reflects a distribution of the activities of the macroblocks, it is possible to suppress a local change of the quantization step value.