Implicit Sub-Block Transform Positioning for Lower Bitstream Overhead
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Solution Overview
Problem
Existing video coding systems face inefficiencies in encoding and decoding processes, particularly in determining sub-block transform positions, leading to increased bitstream overhead and reduced coding efficiency.
Innovation Solution
The method and apparatus for video coding implicitly derive sub-block transform positions based on boundary matching costs, eliminating the need for explicit signaling in the bitstream, thereby optimizing the coding process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If explicit signaling of SBT position is used in the bitstream, then the decoder can accurately determine sub-block transform positions, but the bitstream overhead increases
Solution Approach 1:
The decoder determines the SBT position by independently calculating boundary matching costs using locally available reference picture data and prediction information, without requiring explicit signaling from the encoder. This self-service approach eliminates the need for additional bitstream elements while maintaining accurate position determination.
Solution Approach 2:
Boundary matching cost calculations serve as an intermediary mechanism that bridges the encoder and decoder. Both sides use the same cost calculation methodology to independently arrive at the same SBT position decision, eliminating the need for direct communication of position information through the bitstream.
2Quantity of substance
If implicit derivation of SBT position is used, then the bitstream overhead is reduced, but the computational complexity increases
Solution Approach 1:
The boundary matching cost calculation focuses only on the essential boundary regions that are most indicative of the optimal SBT position. By calculating costs for only the critical boundary areas rather than the entire block, the method achieves sufficient accuracy while limiting the increase in computational complexity.
Solution Approach 2:
Reference picture reconstruction and prediction information are prepared in advance during the normal video decoding process. These pre-computed data structures are then reused for the boundary matching cost calculation, avoiding redundant computations and minimizing the additional computational burden.
3Manufacturing precision
If boundary matching cost calculation is performed for all candidate sub-part blocks, then the most optimal SBT position is identified, but the processing time increases
Solution Approach 1:
Instead of exhaustively evaluating all possible SBT positions, the method calculates boundary matching costs for a selective subset of candidate positions that are most likely to be optimal based on the boundary characteristics. This partial evaluation approach achieves near-optimal results with significantly reduced processing time.
Solution Approach 2:
The boundary matching cost calculation is segmented into distinct regions (top, bottom, left, right boundaries) that are evaluated independently. This segmentation allows for efficient parallel processing and reduces the overall computational time compared to evaluating the entire block as a single unit.
Data Source
AI summary
Methods for implicitly deriving SBT position. According to this method, for a decoder side, an SBT position is determined among a set of candidate sub-part blocks for the current block, where the SBT position is determined implicitly without parsing the SBT position from a bitstream or the SBT position is selected from a partial set of candidate sub-part blocks derived implicitly from the set of candidate sub-part blocks. Transformed residual data are derived for the current block from the encoded data associated with the current block. SBT is applied to the transformed residual data for the current block, by using SBT information comprising the SBT position, to recover reconstructed residual data for the current block. A corresponding method for the encoder side is also disclosed.