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With the wide applications of colored point cloud in many fields, point cloudperceptual quality assessment plays a vital role in the visual communicationsystems owing to the existence of quality degradations introduced in variousstages. However, the existing point cloud quality assessments ignore themechanism of human visual system (HVS) which has an important impact on theaccuracy of the perceptual quality assessment. In this paper, a progressiveknowledge transfer based on human visual perception mechanism for perceptualquality assessment of point clouds (PKT-PCQA) is proposed. The PKT-PCQA mergeslocal features from neighboring regions and global features extracted fromgraph spectrum. Taking into account the HVS properties, the spatial and channelattention mechanism is also considered in PKT-PCQA. Besides, inspired by thehierarchical perception system of human brains, PKT-PCQA adopts a progressiveknowledge transfer to convert the coarse-grained quality classificationknowledge to the fine-grained quality prediction task. Experiments on threelarge and independent point cloud assessment datasets show that the proposed noreference PKT-PCQA network achieves better of equivalent performance comparingwith the state-of-the-art full reference quality assessment methods,outperforming the existed no reference quality assessment network.

Progressive Knowledge Transfer Based on Human Visual Perception Mechanism for Perceptual Quality Assessment of Point Clouds