Decoder Motion Vector Refinement for Video Encoding Efficiency
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Solution Overview
Problem
The increasing demand for high-resolution and high-quality images, such as ultra-high definition (UHD) images, leads to a significant increase in data volume, resulting in higher transmission and storage costs. Existing image encoding/decoding technologies struggle to efficiently handle these high-resolution images.
Innovation Solution
The proposed method enhances the decoder side motion vector refinement (DMVR) technique by refining motion vectors based on a template matching method instead of bilateral matching, and by applying different weights to bidirectional motion vectors. This method improves the accuracy of motion vector refinement and prediction, thereby enhancing encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution and high-quality images are used, then image quality is improved, but data volume increases leading to higher transmission and storage costs
Solution Approach 1:
The patent extracts and removes redundant information from high-resolution images through advanced encoding techniques. By identifying and eliminating unnecessary data components while preserving essential visual quality, the system reduces data volume without compromising image quality, thus resolving the contradiction between high image quality and large data volume.
Solution Approach 2:
The patent employs parameter changes in the encoding process, including transform techniques and quantization strategies that modify how image data is represented. By changing the parameters of data representation rather than the actual image content, the system achieves efficient compression that maintains visual quality while reducing the quantity of data that needs to be transmitted and stored.
2Measurement precision
If decoder side motion vector refinement is applied, then prediction accuracy is improved, but encoding complexity increases
Solution Approach 1:
The patent applies partial action by implementing motion vector refinement selectively rather than uniformly across all blocks. By applying decoder side motion vector refinement only to specific blocks where it provides significant benefit, the system improves prediction accuracy for critical regions while limiting the increase in overall encoding complexity through targeted application.
Solution Approach 2:
The patent implements local quality by applying different levels of motion vector refinement to different regions of the image based on their importance and motion characteristics. High-refinement areas receive more processing to maximize prediction accuracy, while low-importance areas use simpler methods, thus balancing prediction accuracy improvement with encoding complexity management.
Data Source
AI summary
Provided is a video decoding method comprising determining a first base motion vector of a current block for a first reference picture and a second base motion vector of the current block for a second reference picture, determining a first refinement motion vector by refining the first base motion vector by a first motion vector difference and determining a second refinement motion vector by refining the second base motion vector by a second motion vector difference, determining first and second prediction blocks of the current block based on the first refinement motion vector and the second refinement motion vector, and determining a final prediction block for the current block based on a weighted sum of the first prediction block and the second prediction block.