Full-reference video quality assessment considering structural distortion and no-reference quality evaluation of MPEG video

There has been an increasing need recently to develop objective quality measurement techniques that can predict perceived video quality automatically. This paper introduces two video quality assessment models. The first one requires the original video as a reference and is a structural distortion measurement based approach, which is different from traditional error sensitivity based methods. Experiments on the video quality experts group (VQEG) test data set show that the new quality measure has higher correlation with subjective quality evaluation than the proposed methods in VQEG's Phase I tests for full-reference video quality assessment. The second model is designed for quality estimation of compressed MPEG video stream without referring to the original video sequence. Preliminary experimental results show that it correlates well with our full-reference quality assessment model.

[1]  Alan C. Bovik,et al.  DCT-domain blind measurement of blocking artifacts in DCT-coded images , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[2]  H.R. Wu,et al.  A generalized block-edge impairment metric for video coding , 1997, IEEE Signal Processing Letters.

[3]  Paolo Gastaldo,et al.  Objective assessment of MPEG-video quality: a neural-network approach , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[4]  Stefan Winkler,et al.  Video Quality Experts Group: current results and future directions , 2000, Visual Communications and Image Processing.

[5]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[6]  Scott J. Daly,et al.  Visible differences predictor: an algorithm for the assessment of image fidelity , 1992, Electronic Imaging.

[7]  James Hu,et al.  DVQ: A digital video quality metric based on human vision , 2001 .

[8]  Zhou Wang,et al.  Why is image quality assessment so difficult? , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[9]  Jeffrey Lubin,et al.  A VISUAL DISCRIMINATION MODEL FOR IMAGING SYSTEM DESIGN AND EVALUATION , 1995 .

[10]  Zhou Wang,et al.  Blind measurement of blocking artifacts in images , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).