In today's world of technological advancement higher data rate and data transmission with minimal memory
requirement is gaining importance. At the same time it also important to preserve quality of data to be transferred
from various types of distortions and to assess the quality at the receiving end. Hence perceptual image quality
assessment is becoming more popular. This paper introduces a new approach in domain of no reference image
quality assessment of jpeg compressed images using spatial features .Considering the fact that pixel distortion,
blurring and edge information are important aspects as far as distortions are concern. A novel approach is presented
in this paper to address the distortion categories. The results are obtained in conjunction with the subjective
image quality assessment of train and test image categories. Also results are compared with full reference image
quality assessment technique and parameters of full reference quality assessment. To achieve better accuracy
algorithm is tested on LIVE Texas' image database and our own developed database. Results are found to be well
correlating to the various parameters considered for the comparison purpose.
This work is licensed under a Creative Commons Attribution 4.0 International License.
- Ferzliand, R. and Karam, L. 2009. A no-reference objective image sharpness metric based on the notion of just noticeable blur (jnb). IEEE Transactions on Image Processing vol.18, no. 4, pp.717.
- ITU-RRecommendationBT.500-10. 2019. Methodology for the subjective assessment of the quality of television pictures. International Telecommunication Union
- Moorthy, A. K. and Bovik, A. C. 2010. A two-step framework for constructing blind image quality indices. IEEE Signal Processing Letters vol.17, no. 2, pp.587–599.
- Sazzad, Z., Y.Kawayoke, and Y.Horita. 2007. Spatial features based no reference image quality assessment for jpeg2000. Proceedings of the IEEEICIP,Texas,US.
- Sheikh, H. R. and Bovik, A. C. 2006. Image information and visual quality. IEEE Transactions on Image Processing vol. 15, no. 2, pp.430–444.
- Sheikh, H. R., Bovik, A. C., and Cormack, L. K. 2005. No-reference quality assessment using natural scene statistics:jpeg2000. IEEE Transactions on Image Processing vol. 14, no. 11, pp.1918–1927.
- Sheikh, H. R., Wang, Z., Cormack, L., and Bovik, A. C. 2003. Live image quality assessment database. http://live.ece.utexas.edu/research/quality.
- Wang, Z. and Bovik, A. C. 2002. Why is image quality assessment so difficult? IEEE Signal Processing Letters.
- Wang, Z., Sheikh, H. R., and Bovik, A. C. 2002. No-reference perceptual quality assessment of jpeg compressed images. Proc. of IEEE Int. Conf. on Image Processing vol. 1, pp.477–480.
- Y.Horita, Y.Kawayoke, and Sazzad, Z. Image quality evaluation database. hftp://[email protected]/i.
- Z.Wang, A.C.Bovik, H.R.Sheikh, and E.P.Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing vol. 13, pp.600–612.