A usability-based subjective remote sensing image quality assessment database

The current rate of success in launching satellites and advances in onboard and ground image processing have led to a dramatic increase in the scale of remote sensing image data. This has resulted in considerable research on how to provide the best quality of experience to end users. However, subjective image quality assessment (IQA) is time-consuming, cumbersome, expensive and cannot be implemented automatically using computers. Thus, subjective IQA may not be suitable for application requirements. In this paper, we design and construct a usability-based subjective remote sensing IQA database. A corresponding no-reference IQA method is also proposed. The new IQA method uses scale-invariant feature transforms to form a dictionary and then a support vector machine to obtain an IQA model. The experimental results show that the new subjective IQA database is highly suited to the task, and the quality predictions of the new IQA method correlate well with human subjective scores in the new database.

[1]  Guoqing Sun,et al.  Extraction of ground surface elevation from ZY-3 winter stereo imagery over deciduous forested areas , 2015 .

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Damon M. Chandler,et al.  No-reference image quality assessment based on log-derivative statistics of natural scenes , 2013, J. Electronic Imaging.

[4]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[5]  Huili Gong,et al.  Topographic Correction of ZY-3 Satellite Images and Its Effects on Estimation of Shrub Leaf Biomass in Mountainous Areas , 2014, Remote. Sens..

[6]  B Yang.  On-orbit geometric calibration method of ZY-1 02C panchromatic camera , 2013 .

[7]  J. M. Foley,et al.  Contrast masking in human vision. , 1980, Journal of the Optical Society of America.

[8]  Wang Jianb.  Position Precision Evaluation of Secondary Product of Resource-1 02C Satellite Remote Sensing Image , 2013 .

[9]  Alan C. Bovik,et al.  RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment , 2012, IEEE Transactions on Image Processing.

[10]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[11]  Lei Wang,et al.  Comparative Analysis of GF-1 WFV, ZY-3 MUX, and HJ-1 CCD Sensor Data for Grassland Monitoring Applications , 2015, Remote. Sens..

[12]  Alan C. Bovik,et al.  Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality , 2011, IEEE Transactions on Image Processing.

[13]  Maoteng Zheng,et al.  Multistrip Bundle Block Adjustment of ZY-3 Satellite Imagery by Rigorous Sensor Model Without Ground Control Point , 2015, IEEE Geoscience and Remote Sensing Letters.

[14]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[15]  Driss Aboutajdine,et al.  Reduced reference image quality assessment based on statistics in empirical mode decomposition domain , 2014, Signal Image Video Process..

[16]  Huadong Guo,et al.  Remote sensing of the China Seas , 2014 .

[17]  J. Fung,et al.  Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2.5 , 2015 .

[18]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[19]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[20]  Zhou Wang,et al.  Information Content Weighting for Perceptual Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[21]  Hua Huang,et al.  No-reference image quality assessment based on spatial and spectral entropies , 2014, Signal Process. Image Commun..

[22]  Mislav Grgic,et al.  IQM2: new image quality measure based on steerable pyramid wavelet transform and structural similarity index , 2014, Signal Image Video Process..

[23]  Nick G. Kingsbury,et al.  A distortion measure for blocking artifacts in images based on human visual sensitivity , 1995, IEEE Trans. Image Process..

[24]  Alan C. Bovik,et al.  A Two-Step Framework for Constructing Blind Image Quality Indices , 2010, IEEE Signal Processing Letters.

[25]  David Marr,et al.  VISION A Computational Investigation into the Human Representation and Processing of Visual Information , 2009 .

[26]  Ronghua Ma,et al.  Monitoring lake changes of Qinghai-Tibetan Plateau over the past 30 years using satellite remote sensing data , 2014 .

[27]  David Zhang,et al.  FSIM: A Feature Similarity Index for Image Quality Assessment , 2011, IEEE Transactions on Image Processing.

[28]  Nikolay N. Ponomarenko,et al.  METRICS PERFORMANCE COMPARISON FOR COLOR IMAGE DATABASE , 2008 .

[29]  Christophe Charrier,et al.  Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain , 2012, IEEE Transactions on Image Processing.