Lossless compression of point cloud attributes based on probability modeling with directional predictors

Hibiki Yamamoto; Diego Fujii; Yasuyo Kita; Ichiro Matsuda; Susumu Itoh; Kyohei Unno; Satoshi Komorita

doi:10.1117/12.2666996

25 March 2023 Lossless compression of point cloud attributes based on probability modeling with directional predictors

Hibiki Yamamoto, Diego Fujii, Yasuyo Kita, Ichiro Matsuda, Susumu Itoh, Kyohei Unno, Satoshi Komorita

Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 125921Q (2023) https://doi.org/10.1117/12.2666996
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of

Abstract

3D point cloud data consist of a large number of points with attribute information such as color in addition to geometry information of 3D positions. Since their data size tends to be large, efficient compression methods not only for the geometry but also for the attribute information are desired. In general, attribute information has a spatial correlation depending on local texture of the object surface. However, the conventional adaptive prediction technique, which is popular in 2D image coding, cannot be applied as-is, since distribution of the already encoded samples used for the prediction is usually sparse and irregular. In this paper, we propose a method for designing adaptive predictors on a point-by-point basis using a 3D directional autocorrelation model of the attribute information. The obtained predictors are utilized in the probability model optimization technique for efficient lossless coding of RGB color attribution.

Citation Download Citation

Hibiki Yamamoto, Diego Fujii, Yasuyo Kita, Ichiro Matsuda, Susumu Itoh, Kyohei Unno, and Satoshi Komorita "Lossless compression of point cloud attributes based on probability modeling with directional predictors", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 125921Q (25 March 2023); https://doi.org/10.1117/12.2666996

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