Image Encoding Using Geometric Prediction Mode Boundary Lines
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Solution Overview
Problem
Current image encoding/decoding techniques face inefficiencies in compressing high-resolution and high-quality image data, leading to increased transmission and storage costs due to the large amount of data required.
Innovation Solution
The method involves generating prediction blocks for an image using a sample unit weighted sum of first and second prediction blocks, where the weights are determined based on the location of luma samples and a geometric prediction mode boundary line, allowing for improved compression efficiency through bi-directional inter prediction and geometric inter prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high resolution and quality image data is transmitted or stored, then image quality is improved, but transmission cost and storage cost increase
Solution Approach 1:
The patent applies geometric prediction mode boundary lines to partition the current block into different regions, where different prediction modes are applied to different regions. This changes the parameter of prediction mode selection from uniform to spatially varying, enabling more efficient compression while maintaining high image quality by adapting to local geometric structures in the image data
Solution Approach 2:
The patent segments the current block into multiple regions based on geometric prediction mode boundary lines. Each region is then processed with appropriate prediction modes (intra prediction for some regions, inter prediction for others), allowing differential compression strategies that maintain quality while reducing overall data amount through region-specific optimization
2Productivity
If geometric prediction mode boundary line is used to partition current block, then compression efficiency is improved, but device complexity increases
Solution Approach 1:
The patent introduces dynamic geometric prediction mode boundary lines that can be adjusted based on image content characteristics. The boundary lines are not fixed but adapt to the local geometry of the image data, allowing the encoding system to dynamically select optimal partitioning strategies that improve compression efficiency while managing complexity through adaptive rather than static approaches
Data Source
AI summary
An image encoding/decoding method and apparatus are disclosed. The method of decoding an image according to the present invention, comprises, generating a first prediction block of a current block, generating a second prediction block of the current block and generating a final prediction sample of the current block using a sample unit weighted sum of the first prediction block and the second prediction block, wherein a weight used for the sample unit weighted sum is determined based on a location of a current luma sample and a geometric prediction mode (GPM) boundary line for partitioning the current block.