Perceptual Geometry Distortion Assessment of Compressed 3D Meshes.

Abstract

The evaluation of perceptual quality in 3D mesh compression, particularly for Video-based Dynamic Mesh Coding (V-DMC), is challenged by the scarcity of subject-rated datasets and the high computational cost of full-mesh decoding and the sophisticated visual feature extraction steps. To bridge this gap, we first introduce a novel V-DMC distortion dataset, comprising 16 high-quality original meshes and 400 compressed, textureless variants. We conducted a subjective quality assessment study with 30 participants using the Double Stimulus Impairment Scale (DSIS) method to collect reliable Mean Opinion Scores (MOS). We then propose streamMQ, the first-of-its-kind noreference, bitstream-layer model for perceptual quality assessment of V-DMC compressed meshes. By extracting key geometric features such as quantization parameters and triangle count directly from the compressed bitstream, streamMQ predicts perceptual quality without full decoding. Experimental evaluation and comparison with state-of-the-art methods demonstrate that streamMQ achieves highly competitive quality assessment performance at tiny fractions of computational and storage costs, facilitating real-time and low-storage application environments. The dataset and source code will be made publicly available at https://github.com/HFL01/QDU-GDM.