Point Cloud Transform Coefficient Ordering for Smoother Entropy Coding
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Solution Overview
Problem
Existing point cloud compression technologies face inefficiencies in entropy coding due to abrupt transitions in coefficient envelopes when blocks are divided, leading to suboptimal compression ratios and performance in real-time applications.
Innovation Solution
The proposed method arranges transform coefficients in alternating orders of increasing and decreasing frequency within and across blocks, utilizing region-adaptive hierarchical transforms (RAHT) to enhance entropy coding efficiency by smoothing transitions and improving context adaptation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If transform coefficients are arranged in a conventional order for point cloud compression, then the encoding process is simple, but compression efficiency is reduced due to abrupt transitions in coefficient envelopes between blocks
Solution Approach 1:
The patent applies local quality by arranging transform coefficients differently in different blocks based on their position. Specifically, odd-numbered blocks use increasing frequency order while even-numbered blocks use decreasing frequency order, creating locally optimized coefficient arrangements that smooth transitions at block boundaries and improve entropy coding efficiency without requiring complete reorganization of all coefficients
Solution Approach 2:
The patent employs inversion by alternating the frequency order direction between adjacent blocks. Instead of using the same increasing frequency order for all blocks, the patent inverts the order for even-numbered blocks (decreasing frequency), which creates smooth transitions at block boundaries and improves the overall coefficient envelope continuity, thereby enhancing compression efficiency
2Speed
If blocks are divided for parallel processing, then processing speed increases, but abrupt transitions at block boundaries reduce entropy coding performance
Solution Approach 1:
The patent maintains the ability to process blocks independently in parallel while improving entropy coding performance by applying local quality optimization at block boundaries. Each block can be processed separately with its own frequency order (increasing for odd blocks, decreasing for even blocks), preserving parallel processing capabilities while smoothing transitions and reducing information loss during entropy coding
Data Source
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AI summary
In an example point cloud decoding method, at least a portion of a bitstream is entropy decoded to obtain an ordered sequence of transform coefficients. The transform coefficients are arranged such that a first set of the transform coefficients for a first block in a point cloud is followed consecutively by a second set of the transform coefficients for a second block in the point cloud; the transform coefficients in the first set are arranged in order of increasing frequency; and the transform coefficients in the second set are arranged in order of decreasing frequency. The first and second blocks in the point cloud are reconstructed using the entropy decoded transform coefficients.