SUMMARYThis paper reports on the image compression tolerability and high implementability of a novel proposed watermarking method that uses a morphological wavelet transform based on max-plus algebra. This algorithm is suitable for embedded low-power processors in mobile devices. For objective and unified evaluation of the capability of the proposed watermarking algorithm, we focus attention on a watermarking contest presented by the IHC, which belongs to the IEICE and investigate the image quality and tolerance against JPEG compression attack. During experiments for this contest, six benchmark images processed by the proposed watermarking is done to reduce the file size of original images to 1/10, 1/20, or less, and the error rate of embedding data is reduced to 0%. Thus, the embedded data can be completely extracted. The PSNR value is up to 54.66 dB in these experiments. Furthermore, when the smallest image size is attained 0.49 MB and the PSNR value become about 52 dB, the proposed algorithm maintains very high quality with an error rate of 0%. Additionally, the processing time of the proposed watermarking can realize about 416.4 and 4.6 times faster than that of DCT and HWT on the ARM processor, respectively. As a result, the proposed watermarking method achieves effective processing capability for mobile processors.

This paper reports on the JPEG compression tolerance of novel proposed digital watermarking algorithm that uses the morphological wavelet transform based on max-plus algebra. For objective evaluation, we investigated the image quality and tolerance against graphical data compression attack of an image during the digital-watermarking contest presented by the Committee for Information Hiding and its Criteria (IHC). During benchmarks of this contest, graphical data compression was done to reduce the file size of original image (45.5 MB) to 1/10 or less and the error rate of embedding data was reduced to 0%. As a result, the embedded data could be completely extracted. The peak signal-to-noise ratio (PSNR) was 54.08-54.66 dB. Afterwards, additional graphical data compression was done to reduce the file size to 1/20 or less of the original picture image and the error rate was reduced to 0%, and the PSNR was 52.75-54.35 dB. By compressing the image by, about 99%, the smallest data size attained was 0.49 MB and the PSNR was about 52 dB and maintained very high image quality with 0% of error rate. From these results, the processing time of the proposed algorithm was confirmed to be about 2.2 to 148.6 times faster than that of conventional algorithms on embedded systems.