Magnitude Coding for BVD Candidate Subsets in Video Encoding
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Solution Overview
Problem
Current video encoding and decoding systems face challenges in efficiently compressing and decompressing video data, leading to high computational complexity and resource requirements due to the large data sizes associated with video sequences.
Innovation Solution
The proposed solution involves predictive encoding and decoding techniques, specifically by selecting a subset of representative block vector difference (BVD) candidates based on cost criteria, such as whether the endpoint of a BVD candidate is outside a reference region, to reduce computation complexity and improve entropy encoding efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of BVD candidates are used for predictive encoding, then prediction accuracy and video quality are improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts and selects only the most representative BVD candidates from the full set based on specific criteria (endpoint outside reference region, cost function evaluation). This extraction reduces the candidate set to a manageable subset that maintains prediction accuracy while significantly lowering computational complexity during encoding and decoding operations.
Solution Approach 2:
The patent applies different quality criteria to different BVD candidates based on their local characteristics (position, cost function value, endpoint location). By evaluating and selecting candidates based on their individual properties rather than treating all candidates uniformly, the system optimizes prediction accuracy for each local case while controlling overall computational load.
2Measurement precision
If all BVD candidates are processed for entropy encoding, then coding accuracy is maintained, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by processing only a subset of BVD candidates rather than all candidates. The selection criteria (endpoint outside reference region, cost function thresholds) ensure that the partial set processed is sufficient to maintain coding accuracy, avoiding the excessive computation of evaluating every possible candidate.
3Device complexity
If a subset of BVD candidates is selected based on cost criteria, then computational complexity is reduced, but prediction accuracy may deteriorate
Solution Approach 1:
The patent incorporates feedback mechanisms where the selected BVD candidates are evaluated based on cost functions and endpoint conditions, and the selection process adapts based on these feedback results. This ensures that the reduced subset maintains sufficient prediction accuracy by continuously validating the selection criteria against the actual prediction performance.
Data Source
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AI summary
A video may be compressed or decompressed using predictive encoding or decoding. The predictive encoding or decoding may involve entropy coding or decoding magnitude symbols of a block vector difference (BVD), for example, based on a plurality of BVD candidates. A subset of representative BVD candidates may be selected from the plurality of BVD candidates to reduce computation complexity (e.g., cost calculation). The subset of representative BVD candidates may be selected based on one or more subset selection criteria. The BVD may comprise a motion vector difference (MVD).