论文标题
图像质量模型的优化比较图像处理系统
Comparison of Image Quality Models for Optimization of Image Processing Systems
论文作者
论文摘要
客观图像质量评估（IQA）模型的性能主要是通过将模型预测与人类质量判断进行比较来评估的。为此目的收集的知觉数据集为改进IQA方法提供了有用的基准，但是它们的大量使用会产生过度拟合的风险。在这里，我们根据IQA模型对图像处理算法的优化目标进行大规模比较。具体来说，我们使用11个全参考IQA模型来训练深层神经网络，以完成四个低级视觉任务：变形，脱张，超分辨率和压缩。对优化图像进行的主观测试使我们能够根据其感知性能对竞争模型进行排名，阐明其在这些任务中的相对优势和缺点，并提出一组理想的属性，以将其纳入未来的IQA模型。
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments. Perceptual datasets gathered for this purpose have provided useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting. Here, we perform a large-scale comparison of IQA models in terms of their use as objectives for the optimization of image processing algorithms. Specifically, we use eleven full-reference IQA models to train deep neural networks for four low-level vision tasks: denoising, deblurring, super-resolution, and compression. Subjective testing on the optimized images allows us to rank the competing models in terms of their perceptual performance, elucidate their relative advantages and disadvantages in these tasks, and propose a set of desirable properties for incorporation into future IQA models.
