论文标题
使用合成和自然代码手册的图像质量预测：比较结果
Image quality prediction using synthetic and natural codebooks: comparative results
论文作者
论文摘要
我们研究了一个基于构建一组代码矢量的图像/视频质量评估的模型，这些代码向量在某种意义上是图像的某些基本属性，类似于著名的Cornia模型。我们分析了代码簿构建方法，并为其提出了一些修改。同样，从推理时间缩短点开始研究算法。天然图像和合成图像均用于构建代码书，并提供了用于代码书的合成图像的分析。可以证明，如果合成图像用于代码簿构造，则可以改善质量评估的结果。我们还展示了该算法的制度，在这种算法中，在CPU上实时执行与平均意见评分（MOS）的相关性足够高。考虑了各种合并策略以及对比特率敏感性的度量问题。
We investigate a model for image/video quality assessment based on building a set of codevectors representing in a sense some basic properties of images, similar to well-known CORNIA model. We analyze the codebook building method and propose some modifications for it. Also the algorithm is investigated from the point of inference time reduction. Both natural and synthetic images are used for building codebooks and some analysis of synthetic images used for codebooks is provided. It is demonstrated the results on quality assessment may be improves with the use if synthetic images for codebook construction. We also demonstrate regimes of the algorithm in which real time execution on CPU is possible for sufficiently high correlations with mean opinion score (MOS). Various pooling strategies are considered as well as the problem of metric sensitivity to bitrate.
