In July 2024, the research paper titled “Optimized Quantization Parameter Selection for Video-based Point Cloud Compression”, co-authored by the project participants – Prof. Hui Yuan, Dr Xin Lu, and others from Shandong University and De Montfort University, in collaboration with Prof. Ferrante Neri, Dr Linwei Zhu, Prof. Yun Zhang from the University of Surrey, Chinese Academy of Sciences (CAS), China, and Sun Yat-sen University, China, was accepted for publication in Frontiers in Signal Processing. This paper focuses on optimized algorithm development for video-based point cloud compression.
High-quality visualizations of point clouds often require millions of points, resulting in large storage and transmission costs, especially for dynamic point clouds. The video-based point cloud compression (V-PCC) standard generates two-dimensional videos from the geometry and colour information of the point cloud sequence. Each video is then compressed with a video coder, which converts each frame into frequency coefficients and quantizes them using a quantization parameter (QP). Traditionally, the QPs are severely constrained. The rate-distortion performance can be improved by relaxing this constraint and treating the QP selection problem as a multi-variable constrained combinatorial optimization problem, where the variables are the QPs. To solve the optimization problem, we propose a variant of the differential evolution (DE) algorithm. While DE was initially introduced for continuous unconstrained optimization problems, we adapt it for our constrained combinatorial optimization problem. Also, unlike standard DE, we apply individual mutation to each variable. Furthermore, we use a variable crossover rate to balance exploration and exploitation.
Experimental results for the low-delay configuration of the V-PCC reference software show that our method can reduce the average bitrate by up to 43% compared to a method that uses the same QP values for all frames and selects them according to an interior point method.
Reference:
Liu, Q., Yuan, H., Hou, J., Hamzaoui, R., and Su, H. (2021). Model-based joint bit allocation between geometry and color for video-based 3d point cloud compression. IEEE Trans. Multimedia 23, 3278–3291.