ABSTRACT
Blur and noise are two common distortion factors which affect remote sensing image quality. And make it difficult to assess the remote sensing image quality. The Structure Similarity(SSIM) algorithm is simple, high efficient and accurate. However, it does not work well when there is cross distortion in the image. To deal with the problem of SSIM algorithm treating different regions of image identically, this paper considered the perceptual characteristics to different content and masking effect. The proposed method is the perceptual weighting used in the region of interest and based on SSIM algorithm. The experiment shows that, compared with the Peak Signal-Noise Rate(PSNR) index, the proposed index has good consistence with the Structure Similarity(SSIM) index, and can make an effective and correct evaluation of image with both noise and blur. This is an accurate and reliable no-reference remote sensing image quality assessment mothed, which is easy to implement.
- Wang Z, Bovik A C, Lu L. Why is image quality assessment so difficult{C}// IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 2002:IV-3313-IV-3316.Google Scholar
- Li C, Yang X, Chen W, et al. Study on the IQA method for polarization image based on degree of noise pollution{C}// International Conference on Information and Automation. IEEE, 2009:1468--1472.Google Scholar
- Yu S, Sun F, Hongbo L I. No-reference remote sensing image quality assessment method using visual properties{J}. Journal of Tsinghua University, 2013, 53(4):550--555.Google Scholar
- Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli. Image quality assessment: From error visibility to structural similarity. IEEE Transaction on Image Processing, 2004, 13(4):600--612 Google ScholarDigital Library
- Wang Kongqiao, Shen Lansun, Xing Xin, etc. A Quality Assessment Method of Image Based on Visual Interests {J}. Journal of image and Graphics: 2000, 5(4):300--303.Google Scholar
- Lu W, Li X, Gao X, et al. A Video Quality Assessment Metric Based on Human Visual System{J}. Cognitive Computation, 2010, 2(2):120--131.Google ScholarCross Ref
- Yang C, Xu X. Structural similarity highlighting edge regions for image quality assessment{J}. Journal of Image & Graphics, 2011, 16(12):2133--2139.Google Scholar
Index Terms
- No-reference remote sensing image quality assessment based on the region of interest and structural similarity
Recommendations
Content-partitioned structural similarity index for image quality assessment
The assessment of image quality is important in numerous image processing applications. Two prominent examples, the Structural Similarity Image (SSIM) index and Multi-scale Structural Similarity (MS-SSIM) operate under the assumption that human visual ...
Quality assessment on remote sensing image based on neural networks
AbstractImage quality assessment is of great significance for the designment and application of remote sensing systems. CNN based method is proposed for image quality assessment on remote sensing image in this paper. Specifically, we first ...
A Gradient Weighted Structural Similarity Metric for Image Quality Assessment
ICECC '12: Proceedings of the 2012 International Conference on Electronics, Communications and ControlThe assessment of image quality is very important for numerous image processing applications, where the goal of image quality assessment (IQA) algorithms is to automatically assess the quality of images in a manner that is consistent with human visual ...
Comments