Abstract
Based on the Just-Noticeable-Difference (JND) criterion, a subjective video quality assessment (VQA) dataset, called the VideoSet, was constructed recently. In this work, we propose a JND-based VQA model using a probabilistic framework to analyze and clean collected subjective test data. While most traditional VQA models focus on content variability, our proposed VQA model takes both subject and content variabilities into account. The model parameters used to describe subject and content variabilities are jointly optimized by solving a maximum likelihood estimation (MLE) problem. As an application, the new subjective VQA model is used to filter out unreliable video quality scores collected in the VideoSet. Experiments are conducted to demonstrate the effectiveness of the proposed model.
Abstract (translated)
基于Just-Noticeable-Difference（JND）标准，最近构建了一个名为VideoSet的主观视频质量评估（VQA）数据集。在这项工作中，我们提出了一个基于JND的VQA模型，使用概率框架来分析和清理收集的主观测试数据。虽然大多数传统的VQA模型都关注内容的可变性，但我们提出的VQA模型同时考虑了主题和内容的可变性。用于描述主题和内容可变性的模型参数通过求解最大似然估计（MLE）问题而联合优化。作为一种应用，新的主观VQA模型用于过滤掉在VideoSet中收集的不可靠的视频质量分数。进行实验以证明所提出的模型的有效性。
URL
https://arxiv.org/abs/1807.00920
