Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Full Reference Objective Quality Assessment for Reconstructed Background Images

Version 1 : Received: 16 May 2018 / Approved: 17 May 2018 / Online: 17 May 2018 (09:36:33 CEST)

A peer-reviewed article of this Preprint also exists.

Shrotre, A.; Karam, L.J. Full Reference Objective Quality Assessment for Reconstructed Background Images. J. Imaging 2018, 4, 82. Shrotre, A.; Karam, L.J. Full Reference Objective Quality Assessment for Reconstructed Background Images. J. Imaging 2018, 4, 82.


With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.


background reconstruction; image quality assessment; image dataset; subjective evaluation; perceptual quality; objective quality metric


Engineering, Electrical and Electronic Engineering

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0

Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.