1. bookVolume 73 (2022): Issue 2 (April 2022)
Journal Details
License
Format
Journal
eISSN
1339-309X
First Published
07 Jun 2011
Publication timeframe
6 times per year
Languages
English
access type Open Access

Study of subjective and objective quality assessment of infrared compressed images

Published Online: 14 May 2022
Volume & Issue: Volume 73 (2022) - Issue 2 (April 2022)
Page range: 73 - 87
Received: 14 Feb 2022
Journal Details
License
Format
Journal
eISSN
1339-309X
First Published
07 Jun 2011
Publication timeframe
6 times per year
Languages
English
Abstract

Given the lack of accessible infrared compressed images’ benchmarks annotated by human subjects, this work presents a new database with the aim of studying both subjective and objective image quality assessment (IQA) on compressed long wavelength infrared (LWIR) images. The database contains 20 reference (pristine) images and 200 distorted (degraded) images obtained by application of the most known compression algorithms used in multimedia and communication fields, namely: JPEG and JPEG-2000. Each compressed image is evaluated by 31 subjects having different levels of experience in LWIR images. Mean opinion scores (MOS) and natural scene statistics (NSS) of pristine and compressed images are elaborated to study the performance of the database. Five analyses are conducted on collected images and subjective scores, namely: analysis by compression type, analysis by file size, analysis by reference image, analysis by quality level and analysis by subject. Moreover, a wide set of objective IQA metrics is applied on the images and the obtained scores are compared with the collected subjective scores. Results show that objective IQA measures correlate with human subjective results with a degree of agreement up to 95 %, so this benchmark is promising to improve existing and develop new IQA measures for compressed LWIR images. Thanks to a real-world surveillance original images based on which we analyze how image compression and quality level affect the quality of compressed images, this database is primarily suitable for (military and civilian) surveillance applications. The database is accessible via the link: https://github.com/azedomar/compressed-LWIR-images-IQA-database. As a follow-up to this work, an extension of the database is underway to study other types of distortion in addition to compression.

Keywords

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