Multi-Reference-Line Intra Prediction Fusion for Image Compression
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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 the need for high-efficient image compression technologies.
Innovation Solution
An image encoding/decoding method and apparatus utilizing a Multi Reference Line (MRL) approach, which involves obtaining multiple intra prediction modes and reference sample lines to generate and fuse prediction blocks through a weighted sum, improving encoding/decoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-resolution and high-quality images are transmitted and stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent segments the prediction process into multiple reference lines and multiple prediction modes, allowing the image to be predicted using divided reference segments. This segmentation enables more precise local prediction without increasing overall data volume, thus maintaining high image quality while reducing compression overhead and transmission costs.
Solution Approach 2:
The patent dynamically selects the optimal prediction mode and reference line based on local image characteristics. By adaptively choosing from multiple prediction modes (planar, angular, gradient) and multiple reference lines, the system optimizes compression efficiency for different regions, reducing overall data transmission and storage requirements while preserving image quality.
2Productivity
If multiple prediction modes and reference lines are used, then encoding efficiency is improved, but device complexity increases
Solution Approach 1:
The patent applies partial action by using multiple prediction modes and reference lines only where beneficial, rather than applying all modes uniformly. The system selectively engages complex prediction methods for regions that benefit from them while using simpler methods elsewhere, improving encoding efficiency without proportionally increasing device complexity across the entire processing system.
Solution Approach 2:
The patent changes parameters such as the number of reference lines and prediction modes based on local image characteristics and block size. By dynamically adjusting these parameters rather than using fixed complex configurations, the system achieves high encoding efficiency while managing device complexity through adaptive parameter selection rather than permanent structural complexity.
Data Source
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AI summary
An image encoding/decoding method and apparatus are provided. The image decoding method may comprise obtaining a plurality of intra prediction modes and a plurality of reference sample lines of a current block, generating a plurality of prediction blocks of the current block based on the plurality of intra prediction modes and the plurality of reference sample lines, and generating a final prediction block of the current block based on a weighted sum of the plurality of prediction blocks.