CONVIQT: Contrastive Video Quality Estimator.

Perceptual video quality assessment (VQA) is an integral component of many streaming and video sharing platforms. Here we consider the problem of learning perceptually relevant video quality representations in a self-supervised manner. Distortion type identification and degradation level determination is employed as an auxiliary task to train a deep learning model containing a deep Convolutional Neural Network (CNN) that extracts spatial features, as well as a recurrent unit that captures temporal information.

Image Quality Assessment Using Contrastive Learning.

We consider the problem of obtaining image quality representations in a self-supervised manner. We use prediction of distortion type and degree as an auxiliary task to learn features from an unlabeled image dataset containing a mixture of synthetic and realistic distortions. We then train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem.

ST-GREED: Space-Time Generalized Entropic Differences for Frame Rate Dependent Video Quality Prediction.

We consider the problem of conducting frame rate dependent video quality assessment (VQA) on videos of diverse frame rates, including high frame rate (HFR) videos. More generally, we study how perceptual quality is affected by frame rate, and how frame rate and compression combine to affect perceived quality. We devise an objective VQA model called Space-Time GeneRalized Entropic Difference (GREED) which analyzes the statistics of spatial and temporal band-pass video coefficients.

Subjective and Objective Quality Assessment of Stitched Images for Virtual Reality.

We consider the problem of quality assessment (QA) of image stitching algorithms used to generate panoramic images for virtual reality applications. Our contributions are two-fold. We design the Indian Institute of Science Stitched Image QA (ISIQA) database consisting of 264 stitched images and 6600 human quality ratings.