Sparse Image Fidelity Evaluation Based on Wavelet Analysis
Chen Yong Fan Qiang Shuai Feng
(Key Laboratory of Industrial Internet of Things & Network Control, Ministry of Education,Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
To overcome the limitations of traditional image quality assessment methods, which are not well consistent with subjective human evaluation, a quality assessment algorithm of Weighting Sparse Fidelity (WSF) based on wavelet analysis is proposed. The arithmetic simulates nerve network of Human Vision System (HVS) as research point, the image is decomposed with wavelet into four-sub band images, which are divided into blocks at size of , then using Fast Independent Component Analysis training (FastICA) method to train the image blocks. Then, each image block sparse character matrix is extracted to calculate the sparse feature fidelity of the image and build the sparse fidelity quality evaluation model. On this basis, the image is divided into a plurality of interval according to the different details of the visual image information and a visual weight is set in each section, which can be consistent with subjective human evaluation. The experiment results on LIVE database show that the proposed method has a good evaluation of all kinds of distortion types and is highly consistent with human subjective evaluations. The proposed algorithm can effectively simulate the weighted visual cortex of the human visual system perception mechanisms, which compensates for deficiencies of existing image quality assessment methods.
Jiang Gang-yi, Huang Da-jiang, Wang Xu, et al.. Overview on image quality assessment methods[J]. Journal of Electronics & Information Technology, 2010, 32(1): 219-226.
Chen Yong, Li Yuan, Lü Xia-fu, et al.. Active assessment of color image quality based on visual perception[J]. Optics and Precision Engineering, 2013, 21(3): 742-750.
Guo Ying-chun, Yu Ming, and Zhu Qiu-ming. No reference image quality assessment based on subbands similarity and statistical analysis for JPEG2000[J]. Journal of Electronics & Information Technology, 2011, 33(6): 1496-1500.
[4]
Vu P V and Chandler D M. A fast wavelet-based algorithm for global and local image sharpness estimation[J]. IEEE Signal Processing Letters, 2012, 19(7): 423-426.
[5]
Wang Z, Bovik A C, Sheikh H R, et al.. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612.
[6]
Sheikh H R and Bovik A C. Image information and visual quality[J]. IEEE Transactions on Image Processing, 2006, 15(2): 430-444.
[7]
Li C and Bovik A C. Content-partitioned structural similarity index for image quality assessment[J]. Signal Processing: Image Communication, 2010, 25(7): 517-526.
[8]
Zhang L, Zhang D, and Mou X. FSIM: a feature similarity index for image quality assessment[J]. IEEE Transactions on Image Processing, 2011, 20(8): 2378-2386.
Li Ke-meng, Shao Feng, Jiang Gang-yi, et al.. An objective quality assessment of stereoscopic image based on sparse representation[J]. Journal of OptoelectronicsLaser, 2014, 25(11): 2227-2233.
[10]
Bell A J and Sejnowski T J. An information-maximization approach to blind separation and blind deconvolution[J]. Neural Computation, 1995, 7(6): 1129-1159.
[11]
Saad M A and Bovik A C. Natural motion statistics for no-reference video quality assessment[C]. IEEE International Workshop on Quality of Multimedia Experience, San Diego, CA, USA, 2009: 163-167.
[12]
Chang H W, Yang H, Gan Y, et al.. Sparse feature fidelity for perceptual image quality assessment[J]. IEEE Transactions on Image Processing, 2013, 22(10): 4007-4018.
[13]
Sheikh H R, Wang Z, Cormack L, et al.. LIVE image quality assessment database release 2[OL]. http://live.ece.utexas. edu/research/quality. 2014.4.
[14]
VQEG. Final report from the video quality experts group on the validation of objective models of video quality assessment [OL]. ftp://ftp.its.bldrdoc.gov/dist/ituvidq/Boulder_VQEG _jan_04/VQEG_PhaseII_FRTV_Final_Report_SG9060
E.doc, 2003.
[15]
Chandler D M and Hemami S S. VSNR: a wavelet-based visual signal-to-noise ratio for natural images[J]. IEEE Transactions on Image Processing, 2007, 16(9): 2284-2298.
[16]
Sheikh H R, Bovik A C, and De Veciana G. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117-2128.