Point Cloud Vertex Quantization for Bitrate and Visual Quality
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Solution Overview
Problem
Current point cloud compression techniques, such as TriSoup coding scheme, face challenges in achieving stable visual quality and improving objective metrics like PSNR, especially when dealing with dense point clouds.
Innovation Solution
The method involves quantizing the positions of vertices based on quantization parameters and encoding these parameters into the bitstream, allowing for improved bitrate vs. distortion trade-off during encoding and decoding processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If quantization parameters are optimized for vertex positions, then visual quality and PSNR improve, but bitrate increases
Solution Approach 1:
The patent applies parameter changes by optimizing quantization parameters specifically for vertex positions rather than using uniform quantization. The encoder determines quantization parameters for vertices based on their importance and position, allowing differential quantization that improves visual quality for critical vertices while maintaining lower bitrate overall. This resolves the contradiction by making quantization adaptive rather than uniform.
Solution Approach 2:
The patent implements local quality by applying different quantization parameters to different vertices based on their spatial location and importance. Vertices that are more visually significant or located in important regions receive finer quantization (lower quantization step), while less critical vertices use coarser quantization. This local differentiation improves overall visual quality without uniformly increasing bitrate across all vertices.
2Productivity
If compression efficiency is improved through quantization, then bitrate reduces, but visual quality and PSNR deteriorate
Solution Approach 1:
The patent applies dynamics by making the quantization process adaptive rather than static. The encoder dynamically determines quantization parameters for each vertex based on local geometry, vertex importance, and rate-distortion optimization. This dynamic adjustment allows the system to achieve better compression efficiency while maintaining visual quality by allocating bits more intelligently across different vertices rather than using a fixed quantization step.
Solution Approach 2:
The patent implements feedback through rate-distortion optimization in the encoding process. The encoder evaluates the impact of different quantization parameters on both bitrate and visual quality (PSNR), and adjusts quantization parameters accordingly. This feedback mechanism ensures that compression efficiency is improved without excessive degradation of visual quality, as the system continuously optimizes the trade-off between bitrate and reconstruction quality.
Data Source
AI summary
A method of encoding, into a bitstream, positions of points of a point cloud encompassed in a cuboid volume is provided, the positions being represented in a three-dimensional coordinate system, at least three vertices being located on edges of the cuboid volume. The method includes: obtaining quantized positions of the at least three vertices by quantizing the positions of the at least three vertices based on a vertex quantization step that depends on at least one quantization parameter; encoding, into the bitstream, vertex information defining the quantized positions of the at least three vertices; and encoding the at least one quantization parameter into the bitstream.