Stereoscopic Image Compression via Block Matching Confidence
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Solution Overview
Problem
Existing image processing methods face challenges in reducing bit rate while maintaining image quality, particularly in prioritizing moving objects and distinguishing noise from motion, leading to inefficient compression and poor image quality in conditions like darkness, rain, or fog.
Innovation Solution
A method that uses block matching algorithms to determine confidence metrics between pairs of digital images from different views, allowing for variable compression ratios based on the confidence of pixel matches, enabling more efficient encoding of detailed textured regions and noisy areas with lower compression.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If motion-based prioritization is used to allocate higher bit rate to moving objects, then important moving objects are preserved with higher quality, but uninteresting moving objects and noise consume unnecessary bit rate
Solution Approach 1:
The patent applies local quality by differentiating compression strength across different image regions based on their importance. Regions containing important objects are preserved with higher quality (lower compression), while regions with uninteresting content or noise are compressed more aggressively. This is achieved through region segmentation and adaptive quantization parameters that vary spatially across the image.
Solution Approach 2:
The patent segments the image into multiple regions based on content analysis, motion characteristics, and noise detection. By dividing the image into distinct regions with different importance levels, the system can apply differentiated compression strategies to each segment, allocating bit rate efficiently according to regional priority rather than using a uniform approach.
2Loss of energy
If higher compression is applied to reduce bit rate, then storage and transmission efficiency improve, but image quality deteriorates
Solution Approach 1:
The patent implements local quality by applying different compression ratios to different image regions. Important regions (containing objects of interest) are encoded with lower compression to preserve quality, while less important regions (background, uninteresting areas) are encoded with higher compression. This spatially adaptive approach optimizes the overall bit rate while maintaining quality where it matters most.
Data Source
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AI summary
The present invention relates generally to a method and apparatus for controlling a degree of compression of a digital image, and more specifically to such method and apparatus which receives (S802) a stereoscopic digital image, and controls (S810) a degree of compression of the digital image based on block matching characteristics between the two images of the stereoscopic digital image.