Deep Neural Networks for Blind Image Quality Assessment: Addressing the Data Challenge

by   ShahRukh Athar, et al.

The enormous space and diversity of natural images is usually represented by a few small-scale human-rated image quality assessment (IQA) datasets. This casts great challenges to deep neural network (DNN) based blind IQA (BIQA), which requires large-scale training data that is representative of the natural image distribution. It is extremely difficult to create human-rated IQA datasets composed of millions of images due to constraints of subjective testing. While a number of efforts have focused on design innovations to enhance the performance of DNN based BIQA, attempts to address the scarcity of labeled IQA data remain surprisingly missing. To address this data challenge, we construct so far the largest IQA database, namely Waterloo Exploration-II, which contains 3,570 pristine reference and around 3.45 million singly and multiply distorted images. Since subjective testing for such a large dataset is nearly impossible, we develop a novel mechanism that synthetically assigns perceptual quality labels to the distorted images. We construct a DNN-based BIQA model called EONSS, train it on Waterloo Exploration-II, and test it on nine subject-rated IQA datasets, without any retraining or fine-tuning. The results show that with a straightforward DNN architecture, EONSS is able to outperform the very state-of-the-art in BIQA, both in terms of quality prediction performance and execution speed. This study strongly supports the view that the quantity and quality of meaningfully annotated training data, rather than a sophisticated network architecture or training strategy, is the dominating factor that determines the performance of DNN-based BIQA models. (Note: Since this is an ongoing project, the final versions of Waterloo Exploration-II database, quality annotations, and EONSS, will be made publicly available in the future when it culminates.)


page 1

page 14


BIQ2021: A Large-Scale Blind Image Quality Assessment Database

The assessment of the perceptual quality of digital images is becoming i...

Deep Ensembling for Perceptual Image Quality Assessment

Blind image quality assessment is a challenging task particularly due to...

Massive Online Crowdsourced Study of Subjective and Objective Picture Quality

Most publicly available image quality databases have been created under ...

dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

Objective assessment of image quality is fundamentally important in many...

Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment

The research in image quality assessment (IQA) has a long history, and s...

Algorithm Selection for Image Quality Assessment

Subjective perceptual image quality can be assessed in lab studies by hu...

Predicting trends in the quality of state-of-the-art neural networks without access to training or testing data

In many applications, one works with deep neural network (DNN) models tr...

Please sign up or login with your details

Forgot password? Click here to reset