Le Callet Patrick, Viard-Gaudin Christian, Barba Dominique
Institut de Recherche en Communication et Cybernétique de Nantes, University of Nantes, Nantes 44306, France.
IEEE Trans Neural Netw. 2006 Sep;17(5):1316-27. doi: 10.1109/TNN.2006.879766.
This paper describes an application of neural networks in the field of objective measurement method designed to automatically assess the perceived quality of digital videos. This challenging issue aims to emulate human judgment and to replace very complex and time consuming subjective quality assessment. Several metrics have been proposed in literature to tackle this issue. They are based on a general framework that combines different stages, each of them addressing complex problems. The ambition of this paper is not to present a global perfect quality metric but rather to focus on an original way to use neural networks in such a framework in the context of reduced reference (RR) quality metric. Especially, we point out the interest of such a tool for combining features and pooling them in order to compute quality scores. The proposed approach solves some problems inherent to objective metrics that should predict subjective quality score obtained using the single stimulus continuous quality evaluation (SSCQE) method. This latter has been adopted by video quality expert group (VQEG) in its recently finalized reduced referenced and no reference (RRNR-TV) test plan. The originality of such approach compared to previous attempts to use neural networks for quality assessment, relies on the use of a convolutional neural network (CNN) that allows a continuous time scoring of the video. Objective features are extracted on a frame-by-frame basis on both the reference and the distorted sequences; they are derived from a perceptual-based representation and integrated along the temporal axis using a time-delay neural network (TDNN). Experiments conducted on different MPEG-2 videos, with bit rates ranging 2-6 Mb/s, show the effectiveness of the proposed approach to get a plausible model of temporal pooling from the human vision system (HVS) point of view. More specifically, a linear correlation criteria, between objective and subjective scoring, up to 0.92 has been obtained on a set of typical TV videos.
本文描述了神经网络在客观测量方法领域的一种应用，该方法旨在自动评估数字视频的感知质量。这个具有挑战性的问题旨在模拟人类的判断，并取代非常复杂且耗时的主观质量评估。文献中已经提出了几种指标来解决这个问题。它们基于一个通用框架，该框架结合了不同的阶段，每个阶段都解决复杂的问题。本文的目的不是提出一个全局完美的质量指标，而是专注于在简化参考（RR）质量指标的背景下，在这样一个框架中使用神经网络的一种原创方法。特别是，我们指出了这种工具在组合特征并将它们汇总以计算质量分数方面的作用。所提出的方法解决了客观指标固有的一些问题，这些指标应该预测使用单刺激连续质量评估（SSCQE）方法获得的主观质量分数。视频质量专家组（VQEG）在其最近最终确定的简化参考和无参考（RRNR - TV）测试计划中采用了后者。与之前尝试使用神经网络进行质量评估相比，这种方法的独特之处在于使用了卷积神经网络（CNN），它可以对视频进行连续时间评分。在参考序列和失真序列上逐帧提取客观特征；它们源自基于感知的表示，并使用时延神经网络（TDNN）沿时间轴进行整合。在不同的MPEG - 2视频上进行的实验，比特率范围为2 - 6 Mb/s，从人类视觉系统（HVS）的角度展示了所提出的方法获得合理的时间池化模型的有效性。更具体地说，在一组典型的电视视频上，客观评分与主观评分之间的线性相关标准高达0.92。
