Intra Prediction Offsets for High-Resolution Video Decoding
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The increasing demand for high-resolution and high-quality video signals leads to higher data volumes, resulting in increased costs for transmission and storage, and existing image compression techniques are inadequate for efficiently encoding and decoding stereographic image content with high resolution and ultra-high resolution.
Innovation Solution
A method and device for encoding and decoding video signals that hierarchically partition coding blocks, perform intra prediction, and update prediction samples using offsets, allowing for adaptive and efficient encoding and decoding by determining the intra prediction pattern and offset for sub-blocks within a current block.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional image compression techniques are used for high-resolution video, then transmission and storage costs increase, but image quality and resolution requirements cannot be met
Solution Approach 1:
The current block is divided into multiple sub-blocks, and intra prediction is performed separately for each sub-block. This segmentation allows for more precise local prediction, improving image quality while maintaining compression efficiency by adapting to local variations in the image content.
Solution Approach 2:
Different prediction modes and offset corrections are applied to different sub-blocks based on their local characteristics. This local quality approach ensures that each region is predicted with the most appropriate method, improving overall image quality without uniformly increasing data volume across the entire block.
2Measurement precision
If high-resolution and ultra-high resolution stereographic image content is provided, then data transmission and storage costs increase, but image compression efficiency is insufficient with conventional techniques
Solution Approach 1:
The patent applies offset corrections to prediction samples based on gradient calculations and sub-block positions. By dynamically adjusting prediction parameters (offsets) according to local image characteristics, the method achieves better compression efficiency for high-resolution content without sacrificing image quality.
Solution Approach 2:
The intra prediction process is made dynamic by calculating gradients and determining different offset values for different sub-blocks based on their positions and characteristics. This dynamic adaptation allows the compression algorithm to efficiently handle varying content types within high-resolution images.
3Measurement precision
If intra prediction is performed on the entire current block, then encoding complexity increases, but prediction accuracy may be insufficient for different regions
Solution Approach 1:
The current block is divided into multiple sub-blocks for separate prediction processing. This segmentation improves prediction accuracy by capturing local variations, while the systematic approach to sub-block processing keeps encoding complexity manageable through reuse of reference samples and gradient calculations.
Solution Approach 2:
Offset correction is applied selectively based on prediction mode and block characteristics rather than uniformly to all blocks. This partial action approach improves prediction accuracy where needed while avoiding unnecessary complexity in regions where simple prediction suffices.
Data Source
AI summary
Provided is a method for decoding a video signal. The method includes generating a first prediction sample by performing intra prediction on a current block, determining an intra prediction pattern of a current block, partitioning the current block into a plurality of sub-blocks based on the determined intra prediction pattern, generating a first prediction sample for a sub-block by performing intra prediction, determining an offset for the sub-block to obtain a second prediction sample, and generating the second prediction sample for the sub-block by using the first prediction sample and the offset.