Neural network-based assessment of the impact induced in video quality assessment by the semantic labels

Abstract

Subjective video quality assessment generally comes across with semantically labeled evaluation scales (e.g. Excellent, Good, Fair, Poor and Bad on a single stimulus, 5 level grading scale). While suspicions about an eventual bias these labels induce in the quality evaluation always occur, to the best of our knowledge, very few state-of-the-art studies target an objective assessment of such an impact. Our study presents a neural network solution in this respect. We designed a 5-class classifier, with 2 hidden layers, and a softmax output layer. An ADAM optimizer coupled to a Sparse Categorical Cross Entropy function is subsequently considered. The experimental results are obtained out of processing a database composed of 440 observers scoring about 7 hours of video content of 4 types (high-quality stereoscopic video content, low-quality stereoscopic video content, high-quality 2D video, and low-quality 2D video). The experimental results are discussed and confrontment to the reference given by a probability-based estimation method. They show an overall good convergence between the two types of methods while pointing out to some inner applicative differences that are discussed and explained.