JPEG PLENO LEARNING-BASED POINT CLOUD CODING: A PERFORMANCE ANALYSIS

In this paper, a stability analysis of the JPEG Pleno Learning-based Point Cloud Coding Verification Model (VmUC) is performed. The codec is a deep learning-based solution that is able to compress both color and geometry. Three different training sessions were conducted using the default training set and cost function, and six point clouds were encoded/decoded with the resulting operating points for six target distortion/bitrate ratios. The VmUC performance was compared with the MPEG codecs V-PCC and G-PCC, considering three objective metrics, notably PSNR MSE D1, PSNR MSE D2, and PCQM. PSNR MSE D1 was also computed at each training epoch for the six decoded point clouds. It is concluded that the VmUC is able to outperform G-PCC and V-PCC in geometry encoding. However, it is outperformed by V-PCC in terms of color encoding, namely across all three training sessions. Furthermore, it is also shown that the codec does not present a high level of stability, changing its performance considerably with different training sessions.