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Alan C. Bovik

12 accepted papers

2026

Seeing Beyond 8bits: Subjective and Objective Quality Assessment of HDR-UGC Videos

CVPR 2026

High Dynamic Range (HDR) user-generated (UGC) videos are rapidly proliferating across social platforms, yet most perceptual video quality assessment (VQA) systems remain tailored to Standard Dynamic Range (SDR). HDR's higher bit depth, wide color gamut, and elevated luminance range expose distortion

Cited by 0SourcecodeScholar
2023

Re-IQA: Unsupervised Learning for Image Quality Assessment in the Wild

CVPR 2023poster

Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality f…

2022

No-Reference Quality Assessment of Variable Frame-Rate Videos Using Temporal Bandpass Statistics

ICASSP 2022accepted

Recent advances in mobile devices and cloud computing techniques have made it possible to capture, process, and share high resolution, high frame rate (HFR) videos across the Internet nearly instantaneously. Being able to monitor and control the quality of these streamed videos can enable the de-liv…

Cited by 0SourceScholar
2021

Regression or classification? New methods to evaluate no-reference picture and video quality models

ICASSP 2021accepted

Video and image quality assessment has long been projected as a regression problem, which requires predicting a continuous quality score given an input stimulus. However, recent efforts have shown that accurate quality score regression on real-world user-generated content (UGC) is a very challenging…

Cited by 0SourceScholar
2020

Adversarial Video Compression Guided by Soft Edge Detection

ICASSP 2020accepted

We propose a video compression framework using conditional Generative Adversarial Networks (GANs). We rely on two encoders: one that deploys a standard video codec and another one which generates low-level soft edge maps. For decoding, we use a standard video decoder as well as a decoder that is tra…

Cited by 0SourceScholar
2020

BBAND INDEX: A NO-REFERENCE BANDING ARTIFACT PREDICTOR

ICASSP 2020accepted

Banding artifact, or false contouring, is a common video compression impairment that tends to appear on large flat regions in encoded videos. These staircase-shaped color bands can be very noticeable in high-definition videos. Here we study this artifact, and propose a new distortion-specific no-ref…

Cited by 0SourceScholar
2019

Optimal Feature Selection for Blind Super-resolution Image Quality Evaluation

ICASSP 2019accepted

The visual quality of images resulting from Super Resolution (SR) techniques is predicted with blind image quality assessment (BIQA) models trained on a database(s) of human rated distorted images and associated human subjective opinion scores. Such opinion-aware (OA) methods need a large amount of…

Cited by 0SourceScholar
2018

Second Order Natural Scene Statistics Model of Blind Image Quality Assessment

ICASSP 2018accepted

The univariate statistics of bandpass-filtered images provide powerful features that drive many successful image quality assessment (IQA) algorithms. Bivariate Natural Scene Statistics (NSS), which model the joint statistics of multiple bandpass image samples also provide potentially powerful featur…

Cited by 0SourceScholar
2017

Statistics of natural fused image distortions

ICASSP 2017accepted

The capability to automatically evaluate the quality of long wave infrared (LWIR) and visible light images has the potential to play an important role in determining and controlling the quality of a resulting fused LWIR-visible image. Extensive work has been conducted on studying the statistics of n…

Cited by 0SourceScholar
2017

Subjective and objective quality assessment of Mobile Videos with In-Capture distortions

ICASSP 2017accepted

We designed and created a new video database that models a variety of complex distortions generated during the video capturing process on hand-held mobile capturing devices. We describe the content and characteristics of the new database, which we call the LIVE Mobile In-Capture Video Quality Databa…

Cited by 0SourceScholar