Multi-reference Masked Video Block Prediction
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Solution Overview
Problem
Digital video streams consume significant computing and communication resources due to their large data size, and existing compression techniques are not fully effective in reducing data without compromising video quality.
Innovation Solution
The method employs multi-reference compound prediction using masking to segment video frames into segments, where different reference frames and motion vectors are used for each segment, allowing for adaptive prediction and encoding, thereby reducing the data required for the video bitstream.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If existing compression techniques are used, then data size is reduced to some extent, but video quality is compromised
Solution Approach 1:
The current block is divided into multiple segments using a mask, where each segment can be predicted using different reference frames. This segmentation allows the encoder to adaptively select the best reference frame for each segment, maintaining prediction accuracy (video quality) while enabling more efficient compression through selective reference frame usage.
Solution Approach 2:
Different segments of the current block are assigned different reference frames based on their local characteristics. This local quality approach ensures that each segment uses the most appropriate reference frame for accurate prediction, maintaining overall video quality while optimizing compression efficiency through localized adaptation.
2Productivity
If multi-reference compound prediction with masking is used, then coding efficiency is improved, but computational complexity increases
Solution Approach 1:
By segmenting the current block into multiple regions with different mask values, the encoder can apply different prediction strategies to different segments. This segmentation improves coding efficiency by adapting to local variations in the video content, while the mask-based approach provides a systematic framework for managing the complexity of multi-reference frame processing.
Solution Approach 2:
The mask values are determined dynamically based on the characteristics of the current block and available reference frames. This dynamic adaptation allows the system to optimize prediction accuracy for each block while avoiding unnecessary computational overhead in regions where simple prediction suffices, thus balancing coding efficiency and computational complexity.
Data Source
AI summary
Multi-reference compound prediction using masking for encoding and decoding blocks of video frames is described. A mask for segmenting a current block of a current frame of the video sequence into at least a first segment and a second segment is determined using a first set of reference frames. A first predictor using the first set of reference frames is generated for the first segment of the current block. A second predictor is generated for the second segment of the current block using a second set of reference frames, wherein the first set of reference frames is a subset of the second set of reference frames.