VRL-IQA: Visual Representation Learning for Image Quality Assessment

Abstract

The growing adoption of digital multimedia devices and the greater reliance on compression and wireless channels for data transmission has brought renewed focus to the traditional challenge of evaluating image quality. Image Quality Assessment (IQA) is needed to optimize bit rate, compression, or processing and communication strategies for these multimedia technologies. Visual representation learning enables the model to undertake upstream training on large-scale data and then fine-tune the model on downstream data using fewer training samples. Data annotation for IQA is expensive due to the difficulty of grading a picture's quality, the need to gather quality labels from numerous observers, and the diversity of perceptual quality and content of the images. This challenge has limited the amount of the labeled training dataset for IQA to a few thousand. In this study, a deep Convolutional Neural Network is trained on a largescale image dataset produced by simulating 165 distortion scenarios on 150,000 images resulting in 24.75 million distorted images. These images are labeled via an ensemble of full-reference quality assessment models which assign the quality rating to each distorted image by using its reference image. This trained model is fine-tuned on two datasets TID2013 and Kadid-10K datasets containing simulated distortions and two datasets KonIQ-10K and BIQ2021 containing authentic distortions. The fine-tuning performance has resulted in state-of-the-art IQA performance and yielded a Spearman's correlation coefficient of 0.921, 0.893, 0.884, and 0.793, respectively. Moreover, comparison with the ImageNet pre-trained model revealed that the proposed VRL-IQA model provides higher performance in terms of Pearson and Spearman's correlations and achieves the validation criteria with fewer epochs than the ImageNet pre-trained model. These findings contribute to the advancement of IQA, offering a promising approach for robust and accurate quality prediction in various applications.

Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments

“…The incorporation of technology and machine learning [4,5] into video surveillance, particularly in 5G and IoT environments, initiates unprecedented possibilities. Automated video surveillance systems controlled by computer vision algorithms [6][7][8] detect anomalies, changes in motion, and intrusions in real-time, reducing reliance on human monitoring [9]. However, challenges persist, such as operator errors, false alarms, and limitations in contextual information within video footage [10][11][12].…”