Optical Flow Refinement for Accurate Block Boundary Prediction
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Solution Overview
Problem
Existing video coding systems face challenges in accurately predicting block boundaries, leading to inefficiencies in compression and transmission of digital video signals.
Innovation Solution
Implementing block boundary prediction refinement with optical flow (BBPROF) for sub-blocks, which involves decoding a block based on motion vectors and spatial gradients, and using an MV difference to calculate motion vector offsets, enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional block-based motion compensation is used, then computational complexity is reduced, but prediction accuracy at block boundaries deteriorates
Solution Approach 1:
The current block is divided into multiple sub-blocks, and motion compensation is performed independently for each sub-block. This segmentation allows for more precise boundary prediction by capturing local motion variations within each sub-block, while the overall complexity remains manageable due to the regular structure of the subdivision.
Solution Approach 2:
Different motion characteristics are applied to different regions of the block. By calculating motion vectors for each sub-block individually and using optical flow to refine boundary predictions, the method adapts to local motion patterns rather than applying a single global motion model, thereby improving boundary accuracy.
2Measurement precision
If sub-block based motion compensation with optical flow is applied, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
Optical flow is calculated preliminarily for the entire block before dividing into sub-blocks. This preliminary optical flow calculation provides a foundation that guides subsequent sub-block motion compensation, reducing the overall computational burden compared to calculating optical flow independently for each sub-block.
Solution Approach 2:
Optical flow is applied selectively at block boundaries rather than throughout the entire block. This partial application focuses computational resources where they are most needed (at boundaries where prediction errors occur) while avoiding unnecessary calculations in interior regions, thus balancing accuracy improvement with computational complexity.
Data Source
AI summary
Systems, methods, and instrumentalities are disclosed for sub-block/block refinement, including sub-block/block boundary refinement, such as block boundary prediction refinement with optical flow (BBPROF). A block comprising a current sub-block may be decoded based on a sample value for a first pixel that is obtained based on, for example, an MV for a current sub-block, an MV for a sub-block adjacent the current sub-block, and a sample value for a second pixel adjacent the first pixel. BBPROF may include determining spatial gradients at pixel(s)/sample location(s). An MV difference may be calculated between a current sub-block and one or more neighboring sub-blocks. An MV offset may be determined at pixel(s)/sample location(s) based on the MV difference. A sample value offset for the pixel in a current sub-block may be determined. The prediction for a reference picture list may be refined by adding the calculated sample value offset to the sub-block prediction.