A survey of hybrid MC/DPCM/DCT video coding distortions
Signal Processing - Special issue on image and video quality metrics
Issues in vision modeling for perceptual video quality assessment
Signal Processing
No-reference image quality assessment based on DCT domain statistics
Signal Processing
A simplified human vision model applied to a blocking artifact metric
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
No-Reference perceptual quality assessment of JPEG images using general regression neural network
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
No-Reference Estimation of the Coding PSNR for H.264-Coded Sequences
IEEE Transactions on Consumer Electronics
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics
IEEE Transactions on Image Processing
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
IEEE Transactions on Image Processing
A perceptually relevant approach to ringing region detection
IEEE Transactions on Image Processing
Perceptual considerations for motion blur rendering
ACM Transactions on Applied Perception (TAP)
Causes and subjective evaluation of blurriness in video frames
Image Communication
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Compressed video is degraded in quality due to the introduction of coding artifacts. A two-step subjective experiment was performed to evaluate the most visible artifacts and their relation to video quality for AVS and H.264 compressed video. In the first step, non-expert viewers were requested to score the image quality degradation as a function of compression ratio for various video sequences and to indicate which artifact was perceived during scoring. During the second step, eight trained viewers were asked to score the strength of three artifacts, i.e., blurring, blocking, and color distortion, which were reported as the most perceivable artifacts in the first step of the experiment. The quality performance between AVS and H.264 was also compared. The analysis of covariance indicated that the quality performance between AVS and H.264 was very close. A linear regression analysis showed that for the CIF videos 96% of the variance in quality degradation could be predicted by linearly combining the normalized strengths of the three most visible artifacts.