Motion Vector Difference Prediction for Lower Video Bitrate
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Solution Overview
Problem
Existing video coding technologies face inefficiencies in compressing and transmitting large video sequences due to redundant information, particularly in handling complex scenes with varying motion and affine transformations, leading to increased bitrate and reduced video quality.
Innovation Solution
Implementing a method for selecting motion vector difference (MVD) symbols for prediction using advanced tree partitioning techniques, such as quadtree+multi-type trees, and context-based adaptive binary arithmetic coding (CABAC) to enhance entropy encoding, thereby reducing redundant data and improving compression efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of substance
If advanced tree partitioning techniques and context-based adaptive binary arithmetic coding are implemented for MVD symbol prediction, then compression efficiency is improved and bitrate is reduced, but device complexity increases
Solution Approach 1:
The current block is divided into multiple sub-blocks using quadtree and multi-type tree partitioning. Each sub-block is encoded independently with its own MVD symbols, allowing fine-grained compression optimization while managing complexity through modular processing of smaller units
Solution Approach 2:
Context-based adaptive binary arithmetic coding dynamically adjusts coding parameters based on local characteristics of MVD symbols. The encoder adapts probability models and coding strategies according to the specific patterns observed in different regions and contexts, optimizing compression for each local area
2Reliability
If advanced tree partitioning techniques are used to encode complex scenes with varying motion, then video quality is maintained, but encoding time increases
Solution Approach 1:
Complex scenes are divided into multiple sub-blocks through tree partitioning, allowing different motion characteristics to be captured in different regions. This segmentation enables accurate representation of varying motion without requiring overly complex global models
Solution Approach 2:
The encoder applies advanced partitioning and adaptive coding selectively to regions that benefit most from it, rather than uniformly across the entire picture. This partial application maintains video quality where needed while reducing overall encoding time
Data Source
AI summary
A decoder determines first symbols and second symbols available for prediction of respective first and second motion vector difference (MVD) components of a first reference picture list (RPL) and a second RPL. The decoder selects, based on the first and the second symbols available for prediction, one of the first RPL or the second RPL. The decoder selects, within each of the first MVD components of the selected RPL, a subset of most significant symbols for decoding. The decoder entropy decodes, from a bitstream for each symbol of the selected subset, an indication of whether a value of the each symbol is equal to a value of a corresponding symbol of an MVD predictor. The decoder determines, for the each symbol of the selected subset, a value of the each symbol based on the indication and a value of the corresponding symbol of the MVD predictor.