Local Binary Patterns and Neural Networks for No-Reference Image and Video Quality Assessment

Abstract

In the modern world, where multimedia is predicted to form 86% of traffic transmitted over the telecommunication networks in the near future, content providers are looking to shift towards Quality of Experience, rather than Quality of Service in multimedia delivery. Thus, no-reference image quality assessment and the related video quality assessment remaining open research problem, with significant market potential. In this paper we describe a study focused on evaluating the applicability of Local Binary Patterns (LBP) as features and neural networks as estimators for image quality assessment. We focus on blockiness artifacts, as a prominent effect in all block-based coding approaches and the dominant artifact in occurring in videos coded with state-of-the-art video codecs (MPEG-4, H.264, HVEC). In this initial study we show how an LBP-inspired approach, tuned to this particular effect, can be efficiently used to predict the MOS of JPEG coded images. The proposed approach is evaluated on a well-known public database and against widely-used features. The results presented in the paper show that the approach achieves superior performance, which forms a sound basis for future research aimed at video quality assessment and precise blocking artifact detection with sub-frame precision. © Springer-Verlag Berlin Heidelberg 2013.