Research Papers

Remote sensing image compression assessment based on multilevel distortions

[+] Author Affiliations
Hongxu Jiang

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, Beijing 100191, China

Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing 100191, China

Kai Yang

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, Beijing 100191, China

Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing 100191, China

Tingshan Liu

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, Beijing 100191, China

Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing 100191, China

Yongfei Zhang

Beihang University, School of Computer Science and Engineering, Beijing Key Laboratory of Digital Media, Beijing 100191, China

Beihang University, State Key Laboratory of Virtual Reality Technology and Systems, Beijing 100191, China

J. Appl. Remote Sens. 8(1), 083680 (Feb 04, 2014). doi:10.1117/1.JRS.8.083680
History: Received August 12, 2013; Revised December 25, 2013; Accepted December 31, 2013
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Abstract.  The measurement of visual quality is of fundamental importance to remote sensing image compression, especially for image quality assessment and compression algorithm optimization. We exploit the distortion features of optical remote sensing image compression and propose a full-reference image quality metric based on multilevel distortions (MLD), which assesses image quality by calculating distortions of three levels (such as pixel-level, contexture-level, and content-level) between original images and compressed images. Based on this, a multiscale MLD (MMLD) algorithm is designed and it outperforms the other current methods in our testing. In order to validate the performance of our algorithm, a special remote sensing image compression distortion (RICD) database is constructed, involving 250 remote sensing images compressed with different algorithms and various distortions. Experimental results on RICD and Laboratory for Image and Video Engineering databases show that the proposed MMLD algorithm has better consistency with subjective perception values than current state-of-the-art methods in remote sensing image compression assessment, and the objective assessment results can show the distortion features and visual quality of compressed image well. It is suitable to be the evaluation criteria for optical remote sensing image compression.

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© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

Hongxu Jiang ; Kai Yang ; Tingshan Liu and Yongfei Zhang
"Remote sensing image compression assessment based on multilevel distortions", J. Appl. Remote Sens. 8(1), 083680 (Feb 04, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083680


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