Detecting Source Video Artifacts with Supervised Sparse Filters

Abstract

A variety of powerful picture quality predictors are available that rely on neuro-statistical models of distortion perception. We extend these principles to video source inspection, by coupling spatial divisive normalization with a filterbank tuned for artifact detection, implemented in an augmented sparse functional form. We call this method the Video Impairment Detection by SParse Error CapTure (VIDSPECT). We configure VIDSPECT to create state-of-the-art detectors of two kinds of commonly encountered source video artifacts: upscaling and combing. The system detects upscaling, identifies upscaling type, and predicts the native video resolution. It also detects combing artifacts arising from interlacing. Our approach is simple, highly generalizable, and yields better accuracy than competing methods. A software release of VIDSPECT is available online: http://live.ece.utexas.edu/research/quality/VIDSPECT release.zip for public use and evaluation.