A Transform Coding Strategy for Dynamic Point Clouds

The development of real-time 3D sensing devices and algorithms (e.g., multiview capturing systems, Time-of-Flight depth cameras, LIDAR sensors), as well as the widespreading of enhanced user applications processing 3D data, have motivated the investigation of innovative and effective coding strategies for 3D point clouds. Several compression algorithms, as well as some standardization efforts, has been proposed in order to achieve high compression ratios and flexibility at a reasonable computational cost. This paper presents a transform-based coding strategy for dynamic point clouds that combines a non-linear transform for geometric data with a linear transform for color data; both operations are region-adaptive in order to fit the characteristics of the input 3D data. Temporal redundancy is exploited both in the adaptation of the designed transform and in predicting the attributes at the current instant from the previous ones. Experimental results showed that the proposed solution obtained a significant bit rate reduction in lossless geometry coding and an improved rate-distortion performance in the lossy coding of color components with respect to state-of-the-art strategies.