A Reduced Complexity No-Reference Artificial Neural Network Based Video Quality Predictor
There is a growing need for robust methods for reference free perceptual quality measurements due to the increasing use of video in hand-held multimedia devices. These methods are supposed to consider pertinent artifacts introduced by the compression algorithm selected for source coding. This paper proposes a model that uses readily available encoder parameters as input to an artificial neural network to predict objective quality metrics for compressed video without using any reference and without need for decoding. The results verify its robustness for prediction of objective quality metrics in general and for PEVQ and PSNR in particular. The paper also focuses on reducing the complexity of the neural network.
Muhammad Shahid Andreas Rossholm Benny Lovstrom
Department of Signal Processing, Blekinge Institute of Technology SE-37179 Karlskrona, Sweden
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
527-531
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）