Generating Image Distortion Maps Using Convolutional Autoencoders With Application to No Reference Image Quality Assessment

We present two contributions in this work: 1) a reference-free image distortion map generating algorithm for spatially localizing distortions in a natural scene; and 2) no reference image quality assessment (NRIQA) algorithms derived from the generated distortion map. We use a convolutional autoencoder (CAE) for distortion map generation. We rely on distortion maps generated by the SSIM image quality assessment algorithm as the “ground truth” for training the CAE. We train the CAE on a synthetically generated dataset composed of pristine images and their distorted versions. Specifically, the dataset was created by applying standard distortions such as JPEG compression, JP2K compression, additive white Gaussian noise, and blur to the pristine images. SSIM maps are then generated on a per distorted image basis for each of the distorted images in the dataset and are in turn used for training the CAE. We first qualitatively demonstrate the robustness of the proposed distortion map generation algorithm over several images with both traditional and authentic distortions. We also demonstrate the distortion map's effectiveness quantitatively on both standard distortions and authentic distortions by deriving three different NRIQA algorithms. We show that these NRIQA algorithms deliver competitive performance over traditional databases like LIVE Phase II, CSIQ, TID 2013, LIVE MD, and MDID 2013, and databases with authentic distortions like LIVE Wild and KonIQ-10K. In summary, the proposed method generates high-quality distortion maps that are used to design robust NRIQA algorithms. Furthermore, the CAE-based distortion maps generation method can easily be modified to work with other ground truth distortion maps.