3D Video Disparity Vector Derivation Using Depth Subsets
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Solution Overview
Problem
Current 3D video coding methods face inefficiencies in disparity vector derivation from depth data, leading to high computational complexity and storage requirements, especially when dealing with large depth blocks and potential parsing errors in motion vector prediction.
Innovation Solution
Deriving disparity vectors from subsets of depth block samples, such as four corner samples, and generating additional motion vector candidates by reducing precision or adding offsets to existing candidates, reducing data access and computations while maintaining coding performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If disparity vectors are derived from all depth samples in a depth block, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The patent divides the depth block into multiple regions (e.g., left region and right region) and derives disparity vectors separately for each region. This segmentation allows the system to process fewer samples per region while maintaining overall accuracy, thus reducing computational complexity without sacrificing measurement precision.
Solution Approach 2:
The patent extracts only the necessary depth samples (e.g., boundary samples or region-specific samples) needed for disparity vector derivation, rather than processing all depth samples. This extraction approach reduces the number of computations required while preserving the essential information needed for accurate disparity calculation.
2Productivity
If more motion vector candidates are generated, then coding performance is improved, but bitrate increases
Solution Approach 1:
The patent applies different precision levels to different motion vector candidates. Instead of maintaining full precision for all candidates, it uses reduced precision for certain candidates (e.g., derived candidates) while maintaining full precision for others (e.g., original candidates). This local quality differentiation improves coding efficiency by providing diverse candidates while controlling bitrate through selective precision reduction.
3Measurement precision
If depth data is stored and processed in full resolution, then measurement precision is improved, but storage space increases
Solution Approach 1:
The patent applies partial action by processing only a subset of depth samples (e.g., boundary samples or region-specific samples) rather than all depth samples. This approach maintains sufficient measurement precision for disparity vector derivation while significantly reducing the storage space required for depth data, as the system only needs to store and process the essential subset of samples.
Data Source
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AI summary
A method and apparatus for deriving MVP (motion vector predictor) for a block for three-dimensional video coding or multi-view video coding are disclosed. Embodiments according to the present invention replace an unavailable inter-view MV of one neighboring block with a disparity vector derived from depth data of a subset of a depth block corresponding to one neighboring block. A method and apparatus for generating additional candidates for motion vector prediction associated with Merge mode or AMVP (Inter) mode for a block are disclosed. Embodiments according to the present invention generate one or more additional MVP candidates to add to the MVP list if the MVP list size is less than a given list size. The additional MVP candidates are generated either by reducing precision of an available MVP in the MVP list or by adding an offset to the available MVP in the MVP list.