Digital watermarking is the imperceptible embedding of information into host signals like image, video, audio, text. Watermarking is a digital communication problem. The transmitter embeds, after some data coding, a message (the watermark) into the communication channel (the host signal). Mixing the watermark signal and the host feature is modeled as noise addition. Watermark detection and extraction is performed at the receiver. Generally watermarking is modeled as power-constrained communication with side information at the encoder . The major applications of watermarking are in copyrighting, authentication and metadata embedding. Reversible watermarking (RW) introduces a challenging constraint, namely at detection, to exactly recover not only the watermark, but also the original host
The proposed algorithms will outperform the state-of-the-art algorithms with about 20-30%. For instance, if nowadays schemes provide for the test image Lena a maximum embedding capacity of about 2.8 bpp, we expect to obtain about 3.5-3.6 bpp. We also expect that at the same bit-rate, the newly proposed schemes introduce less distortion than the existing ones (gains of 3-6 dB). Besides algorithms for natural graylevel images, we will investigate extensions for color images, multispectral images, video sequences, as well as for audio signals. These performances cannot be obtained with the present algorithms by simple prediction improvement or some parameter optimization. New innovative algorithms should be developed. We intend to investigate multibit embedding with multiple expansion moduli. Two completely different solutions are foreseen: estimation and optimized selection of moduli and map estimation
The increase in embedding bit-rate will permit the use of RW in complex data hiding applications. A first direction is the improvement of image quality or of image transmission by embedding some appropriate information (depth-based image rendering - DBIR, stereo imaging, 3D images). An original idea is the development of reversible image processing algorithms, i.e., by embedding the information necessary for inverting a certain algorithm into the processed image, the auto-recovery of the original will be possible without any additional data. A second direction is the development of specific annotation applications for medical images, satellite images, etc. If enough bit-rate is available, one will also develop data security applications (robust authentication, fingerprinting, etc.). In our opinion, as soon as RW will provide high embedding bit-rate without annoying artifacts, the interest for RW will considerably increase and more and more applications will be developed.
Both lossless compression and reversible watermarking exploit the redundancy of the signals. The reversible embedding of data into a host can be related to the lossless compression of the host to make room to store some more data. While so far reversible watermarking has taken advantage of the research in lossless compression, we believe that nowadays RW can contribute to the improvement of lossless compression algorithms. A straightforward idea is to use the new efficient predictors developed for RW in lossless compression, too. Another direction appears if one considers the upper bound of the embedding bit-rate into a host and the lossless compression ratio for the host. The problem is if one could improve compression by trading between the volume of data that can be embedded into a host and the space that can be gained by compression. Finally, we intend to combine lossless compression and reversible watermarking into a new concept, reversible compander that reduces the size of the image (subsampling, quantization, etc.) and embeds by RW the information necessary to exactly recover the original full image. Compared with a lossless compressor, the reversible compander would have the advantage that the image content is visible (since data is not encoded). We would expect to obtain reversibility at a compression rate of about 75% of the one provided by the present lossless compressors.
IEEE ISSCS 2019, by: Iasi | July 11 – 12, 2019, International Symposium on Signals, Circuits and Systems
IEEE TSP 2019, by: Budapest | July 1 – 3, 2019, International Conference on Telecommunications and Signal Processing
IEEE ICASSP 2019, by: Brighton | May 12 – 17, 2019, International Conference on Acoustics, Speech and Signal Processing
Signal Processing: Image Communication 2019 | February 2019
Dinu Coltuc chaired the Session IFS-P1: Information forensics and security of EUSIPCO 2018
IEEE ICSTCC 2018, by: SINAIA | October 10 – October 12, 2018, International Conference on System Theory, Control and Computing
IEEE ICIP 2018, by: ATHENA | October 7 – October 10, 2018, International Conference on Image Processing
IEEE EUSIPCO 2018, by: ROME | September 3 – September 7, 2018, European Signal Processing Conference
IEEE ICSP 2018, by: Beijing | August 12 – August 16, 2018, International Conference on Signal Processing
IEEE ICASSP 2018, by: Calgary| April 15 – April 20, 2018, International Conference on Acoustics, Speech and Signal Processing
IEEE EUSIPCO 2017, by: KOS | August 28 – September 2, 2017, European Signal Processing Conference
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