GAP-RBF Based NR Image Quality Measurement for JPEG Coded Images
In this paper, we present a growing and pruning radial basis function based no-reference (NR) image quality model for JPEG-coded images. The quality of the images are estimated without referring to their original images. The features for predicting the perceived image quality are extracted by considering key human visual sensitivity factors such as edge amplitude, edge length, background activity and background luminance. Image quality estimation involves computation of functional relationship between HVS features and subjective test scores. Here, the problem of quality estimation is transformed to a function approximation problem and solved using GAP-RBF network. GAP-RBF network uses sequential learning algorithm to approximate the functional relationship. The computational complexity and memory requirement are less in GAP-RBF algorithm compared to other batch learning algorithms. Also, the GAP-RBF algorithm finds a compact image quality model and does not require retraining when the new image samples are presented. Experimental results prove that the GAP-RBF image quality model does emulate the mean opinion score (MOS). The subjective test results of the proposed metric are compared with JPEG no-reference image quality index as well as full-reference structural similarity image quality index and it is observed to outperform both.
Unable to display preview. Download preview PDF.
- 1.Wang, Z., Sheikh, H.R., Bovik, A.C.: No-reference perceptual quality assessment of JPEG compressed images. In: Proc. ICIP 2002, vol. 1, pp. 477–480 (2002)Google Scholar
- 4.Suthaharan, S.: A perceptually significant block-edge impairment metric for digital video coding. In: Proc. ICASSP 2003, Hong Kong, vol. 3, pp. 681–684 (2003)Google Scholar
- 6.Video Quality Experts Group (VQEG) website: http://www.vqeg.org
- 7.JPEG official site: http://www.jpeg.org/
- 9.Babu, V., Perkis, A.: An HVS-based no-reference perceptual quality assessment of JPEG coded images using neural networks. In: Proc. Int. Conf. on Image Processing (ICIP 2005), Genova, Italy, pp. 433–436 (2005)Google Scholar
- 14.Sheikh, H.R., Wang, Z., Cormack, L., Bovik, A.C.: (Live image quality assessment database) http://live.ece.utexas.edu/research/quality
- 15.Wang, Z., Bovik, A.C., Lu, L.: Why is image quality assessment so difficult? In: Proc. ICASSP 2002, vol. 4, pp. 3313–3316 (2002)Google Scholar