To deal with the huge amount of data in point cloud models, data must be compressed before use.Effective evaluation of quality degradation of the compressed point cloud model with respect to the original is needed. This work defines saliency of a cloud point that well reflects the human visual characteristics as the angular difference between the normal vector at the point and the average of normal vectors in its neighborhood. The difference in saliency between the compressed point cloud model and the original is taken as the objective quality index, which is easy to be calculated. Experiments on point cloud models with various resolutions indicate that the proposed method can give results conform to subjective evaluation.
ZHANG Juan1,2, ZHANG Xi-min1,2, WAN Wang-gen1,2, FANG Zhi-jun1,2
. Saliency Based Quality Evaluation of Point Cloud Model[J]. Journal of Applied Sciences, 2014
, 32(5)
: 441
-446
.
DOI: 10.3969/j.issn.0255-8297.2014.05.001
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