Wiener Filter for Image Coding Prediction
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Solution Overview
Problem
High-resolution and high-quality images require efficient compression techniques to reduce transmission and storage costs, as conventional methods struggle with increased data volume.
Innovation Solution
The implementation of a Wiener filter-based method for improving image coding efficiency by deriving filter coefficients from neighboring blocks and applying them to prediction samples, reducing the amount of data needed for residual signal transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional compression techniques are used for high-resolution images, then transmission and storage costs increase, but image quality and resolution are maintained
Solution Approach 1:
The patent changes the parameter of filter application by selectively applying Wiener filters to prediction samples based on block type and motion vector characteristics. This parameter change enables more efficient compression by adapting the filtering process to specific image regions, reducing the data volume required for high-resolution images while maintaining quality standards.
Solution Approach 2:
The patent applies different filtering strategies to different regions of the image based on local characteristics. By identifying specific block types and motion vector patterns, the system applies Wiener filters only where beneficial, creating local quality improvements in prediction accuracy while reducing overall data requirements for transmission and storage.
2Measurement precision
If Wiener filter is applied to prediction samples, then prediction efficiency improves, but additional information and computational complexity increase
Solution Approach 1:
The patent segments the image processing into distinct block types and applies Wiener filters selectively to specific segments based on their characteristics. This segmentation approach improves prediction accuracy for relevant blocks while avoiding unnecessary filtering in other areas, thereby reducing overall computational complexity despite the enhanced precision where applied.
Solution Approach 2:
The patent applies Wiener filtering partially rather than universally to all prediction samples. By determining which blocks benefit from filtering based on motion vector and block type analysis, the system achieves improved prediction accuracy for critical regions while minimizing the additional computational complexity associated with universal filtering application.
3Reliability
If filtering is applied to all prediction samples, then prediction performance improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis of block types and motion vector characteristics before applying Wiener filters. This preliminary action identifies which prediction samples will benefit from filtering, allowing the system to improve prediction performance for relevant blocks while avoiding unnecessary processing time spent on blocks where filtering would not provide benefit.
Data Source
AI summary
An inter prediction method according to the present invention, which is performed by a decoding apparatus, comprises the steps of: acquiring prediction-related information and residual information from a received bitstream; performing inter prediction on a current block on the basis of the prediction-related information so as to generate prediction samples; generating a list of Wiener filter candidates on the basis of spatially neighboring blocks of the current block, and deriving Wiener filter coefficients for the current block on the basis of candidate blocks in the list of the Wiener filter candidates; filtering the prediction samples on the basis of the derived Wiener filter coefficients; deriving residual samples for the current block on the basis of the residual information; and generating a reconstructed picture on the basis of the filtered prediction samples and the residual samples. The present invention can reduce the amount of data for a residual signal and improve coding efficiency.