Image Encoding Quantization Width Adjustment for Luminance
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Solution Overview
Problem
Current image encoding methods, such as JPEG and JPEG 2000, do not effectively control quantization width based on image luminance, leading to uneven compression errors between bright and dark parts, which deteriorates image quality, especially in high-precision images like medical or remote sensing images.
Innovation Solution
An image encoding apparatus that determines a correction coefficient for each image region based on its luminance, allowing for dynamic adjustment of quantization width to balance compression efficiency and image quality, with the correction coefficient being proportional to the luminance or its square root to optimize compression errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quantization width is controlled in accordance with frequency components (larger for high frequency, smaller for low frequency), then compression efficiency is secured, but image quality in dark parts deteriorates due to uneven signal-to-noise ratio
Solution Approach 1:
The patent applies local quality by making the quantization width dependent on both frequency components and luminance values. Different image regions (bright vs. dark) use different quantization widths, with dark regions using smaller quantization widths to preserve detail and maintain signal-to-noise ratio, while bright regions can use larger quantization widths for better compression efficiency.
Solution Approach 2:
The patent changes the quantization parameter dynamically based on two factors: frequency components and luminance values. The quantization width is adjusted according to the luminance of each image region, creating a luminance-dependent quantization matrix that adapts to local brightness conditions, thereby resolving the contradiction between compression efficiency and image quality in dark parts.
2Device complexity
If uniform quantization width is applied across all image regions, then encoding complexity is reduced, but compression errors are uneven between bright and dark parts leading to overall image quality deterioration
Solution Approach 1:
The patent implements local quality by creating different quantization characteristics for different luminance regions. The quantization width varies according to the luminance value of each image region, ensuring that dark regions receive more precise quantization (smaller width) while bright regions can tolerate coarser quantization (larger width), thus achieving uniform image quality across the entire image.
Solution Approach 2:
The patent introduces dynamics by making the quantization width adaptive rather than static. The quantization process dynamically adjusts the width based on the luminance characteristics of each image region, allowing the encoding system to optimize for image quality in dark parts without significantly increasing overall complexity, as the adaptation follows a systematic luminance-based rule.
Data Source
AI summary
A coefficient conversion unit obtains a conversion coefficient for each image region by performing coefficient conversion for each image region. A luminance distribution extraction unit generates luminance distribution information indicating a luminance of each image region. A correction coefficient determination unit determines a correction coefficient based on the luminance distribution information for each image region. A quantization width correction unit obtains a corrected quantization width for each image region by correcting a quantization width with use of a correction coefficient of an image region for each image region. A scalar quantization unit obtains a quantized conversion coefficient for each image region by quantizing a conversion coefficient of an image region with use of a corrected quantization width for each image region. An entropy encoding unit obtains an encoded conversion coefficient for each image region by performing entropy encoding on a quantized conversion coefficient for each image region.