2 citations · 3 across the 6 of their papers we have counts for
6 papers
Image and Video Quality Assessment using Prompt-Guided Latent Diffusion Models for Cross-Dataset Generalization
Shankhanil Mitra, Diptanu De, Shika Rao +1
The design of image and video quality assessment (QA) algorithms is extremely important to benchmark and calibrate user experience in modern visual systems. A major drawback of the…
Knowledge Guided Semi-Supervised Learning for Quality Assessment of User Generated Videos
Shankhanil Mitra, Rajiv Soundararajan
Perceptual quality assessment of user generated content (UGC) videos is challenging due to the requirement of large scale human annotated videos for training. In this work, we addr…
Learning Generalizable Perceptual Representations for Data-Efficient No-Reference Image Quality Assessment
Suhas Srinath, Shankhanil Mitra, Shika Rao +1
No-reference (NR) image quality assessment (IQA) is an important tool in enhancing the user experience in diverse visual applications. A major drawback of state-of-the-art NR-IQA t…
Test Time Adaptation for Blind Image Quality Assessment
Subhadeep Roy, Shankhanil Mitra, Soma Biswas +1
While the design of blind image quality assessment (IQA) algorithms has improved significantly, the distribution shift between the training and testing scenarios often leads to a p…
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks
Shankhanil Mitra, Saiyam Jogani, Rajiv Soundararajan
Designing learning-based no-reference (NR) video quality assessment (VQA) algorithms for camera-captured videos is cumbersome due to the requirement of a large number of human anno…
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content
Shankhanil Mitra, Rajiv Soundararajan
Completely blind video quality assessment (VQA) refers to a class of quality assessment methods that do not use any reference videos, human opinion scores or training videos from t…