A Transform Coding Strategy for Dynamic Point Clouds

Simone Milani, Enrico Polo, Simone Limuti

Abstract:

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.