Perceptual Quality Assessment of SAR Image Compression Based on Image Content Partition and Neural Network

HU Anzhou; ZHANG Rong; YIN Dong; CHEN Yuan

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(3): 543-548

HU Anzhou, ZHANG Rong, YIN Dong, et al., “Perceptual Quality Assessment of SAR Image Compression Based on Image Content Partition and Neural Network,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 543-548, 2013,

Citation:

HU Anzhou, ZHANG Rong, YIN Dong, et al., “Perceptual Quality Assessment of SAR Image Compression Based on Image Content Partition and Neural Network,” Chinese Journal of Electronics, vol. 22, no. 3, pp. 543-548, 2013,

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Perceptual Quality Assessment of SAR Image Compression Based on Image Content Partition and Neural Network

Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China

Funds: This work is supported by the National Basic Research Program of China (973 Program) (No.2010CB731904).

Received Date: 2012-01-01
Rev Recd Date: 2012-09-01
Publish Date: 2013-06-15

Abstract

Abstract

Image quality assessment (IQA) is of fundamental importance for image compression applications. Traditional IQA measures used for Synthetic aperture radar (SAR) image compression do not consider the properties of Human visual system (HVS). Since human beings are the final users in most SAR image applications, the objective evaluation coordinate to human's perception is the most acceptable and practical IQA method. In this paper, we propose a novel objective approach based on image content partition and Neural network (NN) by introducing the HVS and SAR image characteristics. Experimental results demonstrate that the proposed metric correlates well with subjective quality of SAR image compression and outperforms those state-of-art objective models using Structural similarity index (SSIM), Singular value decomposition (SVD) and Visual information fidelity (VIF).
- Synthetic aperture radar,
- Image quality assessment,
- Image compression,
- Human visual system

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References(16)

References

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