Intra Prediction Filter Selection for Efficient Video Decoding
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images leads to a significant increase in transmission and storage costs due to the rise in the amount of transmitted information, necessitating high-efficient image compression technology.
Innovation Solution
An image encoding/decoding method that selects a filter for intra prediction based on whether filtering of a sample is used, applying smoothing or sharpening filters depending on specific conditions, and generates a bitstream for efficient transmission and storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images are transmitted, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting filtering parameters (smoothing filter strength, sharpening filter strength) based on block characteristics such as gradient magnitude and variance. This allows the encoding system to optimize compression efficiency while maintaining image quality by adapting the filtering parameters to local image content rather than using fixed parameters throughout the image.
2Measurement precision
If filtering is applied to reference samples during intra prediction, then prediction accuracy is improved, but encoding complexity increases
Solution Approach 1:
The patent implements local quality by applying different filtering operations to different blocks based on their local characteristics. Specifically, smoothing filters are applied to blocks with low gradient magnitude (smooth regions), while sharpening filters are applied to blocks with high gradient magnitude (edge regions). This localized approach improves prediction accuracy where needed while avoiding unnecessary filtering in regions where it would add complexity without benefit.
Solution Approach 2:
The patent employs dynamics by making the filtering operation adaptive and condition-based rather than static. The filtering type (smoothing or sharpening) and strength are dynamically selected based on computed metrics such as gradient magnitude and variance for each block. This dynamic adaptation allows the system to optimize prediction accuracy for each local region while managing overall encoding complexity through conditional logic.
3Stability of the object's composition
If smoothing filter is applied to reference samples, then prediction smoothness is improved, but edge sharpness deteriorates
Solution Approach 1:
The patent applies the inversion principle by using opposite filtering approaches (smoothing vs. sharpening) depending on the local image characteristics. Instead of universally applying smoothing filters to improve prediction smoothness, the system inverts the approach by applying sharpening filters to edge regions where smoothness would degrade edge sharpness. This inverse approach ensures that the filtering operation enhances rather than degrades the local image features.
Data Source
AI summary
An image decoding method is disclosed. The image decoding method includes obtaining intra prediction mode information of a current block from a bitstream, determining an intra prediction mode of the current block based on the intra prediction mode information and an intra prediction mode of a neighboring block located around the current block, deriving a value of a first filter flag for use of filtering of an intra prediction reference sample of the current block based on coding parameters for the current block, selecting an interpolation filter to be used for intra prediction of the current block based on the first filter flag, and generating an intra prediction block of the current block using the selected interpolation filter.