用最大熵与量化模型更准预测图像质量评分分布。
Maximum entropy and quantized metric models for absolute category ratings
- 基于均值方差构建连续潜变量的量化模型，拟合评分分布。
- 在KonIQ-10k和VQEG HDTV数据集上优于现有方法，预测未见评分更准。
- 可生成细粒度体验质量分位数，适合服务提供商制定满意度目标。
大多数图像质量评估研究的数据集采用五级分类评分（1为差，5为优）。每个刺激的评分数量从1到5汇总后以平均意见分（MOS）形式给出。本文研究一类由均值和方差参数化的多项分布族，用于拟合经验评分分布。为此，我们提出基于连续分布的量化度量模型，将感知质量建模为潜变量，通过阈值对随机变量进行量化以确定各评分类别的概率。此外，我们引入一种新的离散最大熵分布，给定均值与方差时最大化熵。我们在两个大型数据集——KonIQ-10k 和 VQEG HDTV 上对比了这些模型与现有最优模型（广义评分分布）的表现。对于输入的评分分布，我们拟合的双参数模型能更准确预测未见评分。与经验型平均意见分分布及其离散模型相比，我们的连续模型可提供细粒度的质量体验分位数估计，这对服务提供商满足特定比例用户群体的体验目标具有实际意义。
原文摘要 · Abstract (English)
The datasets of most image quality assessment studies contain ratings on a categorical scale with five levels, from bad (1) to excellent (5). For each stimulus, the number of ratings from 1 to 5 is summarized and given in the form of the mean opinion score. In this study, we investigate families of multinomial probability distributions parameterized by mean and variance that are used to fit the empirical rating distributions. To this end, we consider quantized metric models based on continuous distributions that model perceived stimulus quality on a latent scale. The probabilities for the rating categories are determined by quantizing the corresponding random variables using threshold values. Furthermore, we introduce a novel discrete maximum entropy distribution for a given mean and variance. We compare the performance of these models and the state of the art given by the generalized score distribution for two large data sets, KonIQ-10k and VQEG HDTV. Given an input distribution of ratings, our fitted two-parameter models predict unseen ratings better than the empirical distribution. In contrast to empirical ACR distributions and their discrete models, our continuous models can provide fine-grained estimates of quantiles of quality of experience that are relevant to service providers to satisfy a target fraction of the user population.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。