Selective Local Adaptive Wiener Filter for Video Coding
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Solution Overview
Problem
Conventional video encoding algorithms result in information loss during compression, leading to decreased picture quality, as they conflict between maximizing video quality and compression efficiency.
Innovation Solution
An adaptive Wiener filter is applied locally to selected pixels using histogram segmentation, improving coding efficiency by minimizing distortion and optimizing rate-distortion criteria, allowing for selective filtering based on pixel intensity variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional video encoding algorithms are used to maximize compression, then compression efficiency is improved, but picture quality deteriorates due to information loss
Solution Approach 1:
The patent applies Wiener filtering selectively to specific pixel regions identified through histogram segmentation rather than uniformly across the entire image. This local quality approach allows the system to improve picture quality in regions that benefit from filtering while maintaining compression efficiency in regions where filtering would be redundant or harmful.
Solution Approach 2:
The patent segments the video frame into different pixel intensity regions using histogram analysis. By dividing the image space into multiple bins based on pixel intensity values, the system can apply adaptive filtering only to specific segments (bins) that exhibit characteristics suitable for Wiener filtering, thereby balancing quality improvement with compression efficiency.
2Loss of information
If adaptive Wiener filtering is applied to improve picture quality, then coding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies Wiener filtering partially rather than universally - only to pixel regions that fall within specific histogram bins and meet certain criteria. This partial action approach reduces the overall computational complexity compared to applying filtering to the entire image, while still achieving quality improvement in the most beneficial regions.
Solution Approach 2:
The patent dynamically adjusts filtering parameters based on local image characteristics derived from histogram analysis. By changing filtering parameters adaptively according to pixel intensity distributions in different regions, the system optimizes quality improvement while managing computational complexity through parameter adaptation rather than fixed complex processing.
3Loss of information
If selective pixel filtering is applied based on histogram segmentation, then coding artifacts are reduced, but processing time increases
Solution Approach 1:
The patent performs histogram segmentation and identifies candidate pixel regions for filtering before applying the actual Wiener filtering operation. This preliminary action of pre-selecting regions based on intensity distribution allows the system to avoid unnecessary filtering computations, thereby reducing overall processing time while still effectively reducing coding artifacts in the selected regions.
Data Source
AI summary
An adaptive Wiener filter may be applied to improve coding efficiency because of information lost during quantization of the video encoding process. The Wiener filter may be selectively applied globally to an entire picture or locally to portions of the picture. Histogram segmentation may be used to select pixels for Wiener filtering in some embodiments. The Wiener filter may be adaptively applied to histogram bins, improving coding efficiency in some cases.