Non-binary occupancy map for video point cloud coding
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Solution Overview
Problem
Current video-based point cloud compression technologies face challenges in efficiently compressing high-dimensional point cloud data, which requires significant storage and transmission resources, and existing methods may introduce geometry distortions due to binary occupancy map constraints.
Innovation Solution
The proposed method involves generating a non-binary occupancy map with variable bit depth, allowing each pixel value to represent multiple occupancy patterns, and using a combination of average and median statistics to reduce computational complexity, thereby enabling efficient encoding and decoding of video streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If binary occupancy map is used for point cloud compression, then the compression process is simple and fast, but geometry distortions occur and compression efficiency is limited
Solution Approach 1:
The patent changes the occupancy value parameter from binary (0 or 1) to multi-level (0, 1, 2, or 3), where each value represents different occupancy patterns. This parameter expansion allows the occupancy map to encode more information about point distribution within each block, reducing geometry distortions while maintaining compression efficiency through the use of compact encoding tables for the extended occupancy values.
2Manufacturing precision
If non-binary occupancy map with variable bit depth is used, then compression efficiency and geometry accuracy improve, but computational complexity increases
Solution Approach 1:
The patent performs preliminary classification of occupancy patterns into discrete categories (0, 1, 2, 3) before encoding. By pre-defining what each occupancy value represents in terms of point distribution patterns, the system avoids complex real-time analysis during encoding. The encoder simply determines which predefined pattern matches the block content, significantly reducing computational complexity compared to continuous analysis.
Solution Approach 2:
The patent uses copying by referencing predefined occupancy patterns and their corresponding bit representations. Instead of computing unique encodings for every possible point configuration, the system copies from a set of predefined pattern templates, matching the actual occupancy to the closest template and using its pre-computed encoding. This template-matching approach dramatically reduces computational burden.
3Quantity of substance
If traditional binary occupancy encoding is used, then bit rate is reduced, but the ability to represent complex occupancy patterns is limited
Solution Approach 1:
The patent adds an additional dimension to the occupancy representation by introducing a third level of information: not just whether a block is occupied, but how densely it is occupied. The occupancy values 0, 1, 2, and 3 represent increasing levels of occupancy density or pattern complexity. This dimensional expansion allows the system to represent diverse occupancy patterns using the same compact encoding structure, maintaining low bit rate while increasing versatility.
Data Source
AI summary
Methods and apparatuses of encoding a video stream encoded using video point cloud coding include obtaining a source point cloud; generating an occupancy map including one or more pixels based on the source point cloud, an occupancy value associated with each pixel being a non-binary value; encoding the occupancy map to generate an encoded occupancy map, wherein a block of the encoded occupancy map corresponds to the one or more pixels of the occupancy map; selecting an occupancy value of the block based on the one or more pixels; and generating an encoded video stream based on the selected occupancy value.