TY - GEN
T1 - Image Quality Estimation Using Logarithmic Spread Transform Dither Modulation
AU - Li, Na
AU - Yuan, Xiaochen
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/11/7
Y1 - 2018/11/7
N2 - As a pragmatic and novel application of digital watermarking, image quality estimation has been studied in recent years. In this paper, we propose a watermarking-based image quality evaluation scheme. The Logarithmic Spread Transform Dither Modulation is proposed based on the quantization index modulation and then applied to embed and extract the watermarks. The traditional objective metrics are employed to measure quality of images. We calculate the True Detection Rates (TDR) value to represent degradation of watermark. Considering that the embedded watermark and the watermarked image are distorted simultaneously, the image quality can be evaluated by matching the TDR value with a quality value on a pre-generated curve. Experimental results indicate that the proposed scheme provides a good image quality estimation result. The accuracy of the estimation keeps stabilization under different tested attacks, including JPEG compression, Gaussian noise addition and low-pass filtering.
AB - As a pragmatic and novel application of digital watermarking, image quality estimation has been studied in recent years. In this paper, we propose a watermarking-based image quality evaluation scheme. The Logarithmic Spread Transform Dither Modulation is proposed based on the quantization index modulation and then applied to embed and extract the watermarks. The traditional objective metrics are employed to measure quality of images. We calculate the True Detection Rates (TDR) value to represent degradation of watermark. Considering that the embedded watermark and the watermarked image are distorted simultaneously, the image quality can be evaluated by matching the TDR value with a quality value on a pre-generated curve. Experimental results indicate that the proposed scheme provides a good image quality estimation result. The accuracy of the estimation keeps stabilization under different tested attacks, including JPEG compression, Gaussian noise addition and low-pass filtering.
KW - Image quality estimation
KW - Logarithmic spread transform dither modulation
KW - Quantization index modulation
KW - True detection rates
UR - http://www.scopus.com/inward/record.url?scp=85058061250&partnerID=8YFLogxK
U2 - 10.1109/ICMLC.2018.8526945
DO - 10.1109/ICMLC.2018.8526945
M3 - Conference contribution
AN - SCOPUS:85058061250
T3 - Proceedings - International Conference on Machine Learning and Cybernetics
SP - 282
EP - 287
BT - Proceedings of 2018 International Conference on Machine Learning and Cybernetics, ICMLC 2018
PB - IEEE Computer Society
T2 - 17th International Conference on Machine Learning and Cybernetics, ICMLC 2018
Y2 - 15 July 2018 through 18 July 2018
ER -