Histogram Segmentation Adaptive Wiener Filter for Video Encoding
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Solution Overview
Problem
In lossy video coding, quantization of transform coefficients leads to quality degradation in reconstructed pictures, and global Wiener filters may fail to adapt to local pixel information and constant quality loss within histogram bins.
Innovation Solution
Adaptive Wiener filtering with offset is applied to histogram bins, using histogram segmentation to categorize pixels and determine the best filtering scheme based on Rate Distortion Optimization (RDO) criteria, with filter coefficients and offset values generated and transmitted to the decoder to minimize sum of absolute differences between original and filtered pixel values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a global Wiener filter is applied to reconstructed pictures, then picture quality improvement is achieved, but the filter fails to adapt to local pixel information and constant quality loss within histogram bins
Solution Approach 1:
The patent segments the histogram into multiple bins based on pixel value ranges, allowing different filtering strategies to be applied to different segments. This segmentation enables the filter to adapt to local characteristics within each bin while maintaining computational efficiency through batch processing of segmented data.
Solution Approach 2:
The patent applies local adaptive filtering within each histogram bin by calculating offset values specific to each bin. This allows the filter to maintain different quality characteristics for different pixel value ranges, addressing the constant quality loss problem within bins while improving overall picture quality through localized adaptation.
2Manufacturing precision
If adaptive filtering is applied to compensate for picture quality loss, then reconstructed picture quality improves, but computational complexity and processing overhead increase
Solution Approach 1:
The patent performs preliminary histogram analysis and bin segmentation before applying the Wiener filter. By pre-calculating histogram bins and organizing pixel data into segments, the complex adaptive filtering operation is simplified into more manageable batch operations, reducing overall computational complexity while maintaining adaptation benefits.
Solution Approach 2:
The patent changes the parameter representation by working with histogram bin offsets rather than individual pixel values. This parameter transformation allows the filter to adapt to local characteristics using compact offset values for each bin, significantly reducing the computational burden compared to processing each pixel individually while maintaining picture quality improvement.
3Productivity
If quantization step is increased to improve video compression gain, then compression efficiency improves, but picture quality loss increases
Solution Approach 1:
The patent converts the harmful effect of quantization-induced quality loss into a benefit by using the Wiener filter to statistically reconstruct lost information. The filter exploits the statistical properties of quantized data to recover picture quality, effectively turning the compression artifact into an opportunity for intelligent reconstruction and quality improvement.
Solution Approach 2:
The patent implements a feedback mechanism where the reconstructed picture from the Wiener filter is used to improve subsequent encoding decisions. By analyzing the filtering results and their impact on quality, the system can adjust encoding parameters to maintain better quality at comparable compression ratios, creating a continuous improvement loop.
Data Source
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AI summary
Reconstructed picture quality for a video codec system may be improved by categorizing reconstructed pixels into different histogram bins with histogram segmentation and then applying different filters on different bins. Histogram segmentation may be performed by averagely dividing the histogram into M bins or adaptively dividing the histogram into N bins based on the histogram characteristics. Here M and N may be a predefined, fixed, non-negative integer value or an adaptively generated value at encoder side and may be sent to decoder through the coded bitstream.