  Deep learning techniques have revolutionized the fields of image restoration
and image quality assessment in recent years. While image restoration methods
typically utilize synthetically distorted training data for training, deep
quality assessment models often require expensive labeled subjective data.
However, recent studies have shown that activations of deep neural networks
trained for visual modeling tasks can also be used for perceptual quality
assessment of images. Following this intuition, we propose a novel
attention-based convolutional neural network capable of simultaneously
performing both image restoration and quality assessment. We achieve this by
training a JPEG deblocking network augmented with "quality attention" maps and
demonstrating state-of-the-art deblocking accuracy, achieving a high
correlation of predicted quality with human opinion scores.
