Version 1
: Received: 14 August 2021 / Approved: 18 August 2021 / Online: 18 August 2021 (17:06:02 CEST)
How to cite:
Al Chami, Z.; Abou Jaoude, C.; Chbeir, R. An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time. Preprints2021, 2021080392. https://doi.org/10.20944/preprints202108.0392.v1
Al Chami, Z.; Abou Jaoude, C.; Chbeir, R. An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time. Preprints 2021, 2021080392. https://doi.org/10.20944/preprints202108.0392.v1
Al Chami, Z.; Abou Jaoude, C.; Chbeir, R. An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time. Preprints2021, 2021080392. https://doi.org/10.20944/preprints202108.0392.v1
APA Style
Al Chami, Z., Abou Jaoude, C., & Chbeir, R. (2021). An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time. Preprints. https://doi.org/10.20944/preprints202108.0392.v1
Chicago/Turabian Style
Al Chami, Z., Chady Abou Jaoude and Richard Chbeir. 2021 "An Integration of Deep Network with Random Forests Framework for Image Quality Assessment in Real-Time" Preprints. https://doi.org/10.20944/preprints202108.0392.v1
Abstract
In recent years, data providers are generating and streaming a large number of images. More particularly, processing images that contain faces have received great attention due to its numerous applications, such as entertainment and social media apps. The enormous amount of images shared on these applications presents serious challenges and requires massive computing resources to ensure efficient data processing. However, images are subject to a wide range of distortions in real application scenarios during the processing, transmission, sharing, or combination of many factors. So, there is a need to guarantee acceptable delivery content, even though some distorted images do not have access to their original version. In this paper, we present a framework developed to estimate the images' quality while processing a large number of images in real-time. Our quality evaluation is measured using an integration of a deep network with random forests. In addition, a face alignment metric is used to assess the facial features. Experimental results have been conducted on two artificially distorted benchmark datasets, LIVE and TID2013. We show that our proposed approach outperforms the state-of-art methods, having a Pearson Correlation Coefficient (PCC) and Spearman Rank Order Correlation Correlation Coefficient (SROCC) with subjective human scores of almost 0.942 and 0.931 while minimizing the processing time from 4.8ms to 1.8ms.
Keywords
image quality assessment; real-time image processing; image functions adaptation; convolutional neural network; face alignment; deep neural network; random forest
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
