Results 131 to 140 of about 1,251 (171)
Some of the next articles are maybe not open access.
Tamper Localization and Lossless Recovery Watermarking Scheme
Communications in Computer and Information Science, 2011Tamper localization and recovery watermarking scheme can be used to protect the integrity and authenticity of medical images. In this paper, a simple tamper localization and recovery scheme that uses lossless compression was proposed. Lossy compression may also be applied when necessary.
Siau-Chuin Liew, Jasni Zain
exaly +2 more sources
Lossless image hierarchical recovery based on POB number system
Signal Processing, 2020Abstract This paper proposes an effective method for image tampering localization and recovery. In the proposed scheme, the host image is firstly encrypted by a chaotic system, and then bit-plane separation is conducted on the encrypted image. Afterwards, the certificate watermark and the recovery watermark are calculated respectively on the MSB ...
Tiegang Gao
exaly +2 more sources
A novel lossless recovery algorithm for basic matrix-based VSS
Multimedia Tools and Applications, 2017Lossless recovery is very important for visual secret share (VSS). In this paper, a novel lossless recovery algorithm for the basic matrix-based VSS is proposed. The algorithm has the merit of reconstructing secret image losslessly by using simple addition operation. The algorithm proves that the condition of lossless recovery of the secret image is ΞΎ0
Shen Wang, Weizhe Zhang
exaly +2 more sources
Lossless recovery of a VQ index table with embedded secret data
Journal of Visual Communication and Image Representation, 2007This paper proposes a reversible data embedding scheme based on a VQ image compression technique which emphasizes that the original VQ compressed codes can be recovered after data extraction. In this proposed scheme, a VQ codebook, which had already been clustered into three groups, was adopted to achieve secret concealment and data recovery.
Yu-Chen Hu, Chin-Chen Chang
exaly +2 more sources
Secret image sharing scheme with lossless recovery and high efficiency
Signal Processing, 2023Xuehu Yan +2 more
exaly +2 more sources
FreeLauncher: Lossless Failure Recovery of Parameter Servers with Ultralight Replication
2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), 2021Modern distributed machine learning (ML) systems leverage large-scale computing infrastructures to achieve fast model training. For many servers jointly training a model, failure recovery becomes an important challenge when a training task could be accomplished in minutes rather than days.
Yangyang Zhang 0001 +4 more
openaire +1 more source
Multi-image based self-embedding watermarking with lossless tampering recovery capability
Expert Systems With ApplicationsChuan Qin, Fang Cao, Lin Huang
exaly +2 more sources
On distributed successive refinement with lossless recovery
2014 IEEE International Symposium on Information Theory, 2014The problem of successive refinement in distributed source coding and in joint source-channel coding is considered. The emphasis is placed on the case where the sources have to be recovered losslessly in the second stage. In distributed source coding, it is shown that all sources are successively refinable in sum rate, with respect to any (joint ...
Wang, Chien-Yi, Gastpar, Michael C.
openaire +2 more sources
Universal Compressed Sensing for Almost Lossless Recovery
IEEE Transactions on Information Theory, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shirin Jalali, H. Vincent Poor
openaire +2 more sources
Perceptually Lossless Image Compression with Error Recovery
Proceedings of the 2nd International Conference on Vision, Image and Signal Processing, 2018In many bandwidth constrained applications, lossless compression may be unnecessary, as only two to three times of compression can be achieved. An alternative way to save bandwidth is to adopt perceptually lossless compression, which can attain eight times or more compression without loss of important information.
Chiman Kwan, Eric Shang, Trac D. Tran
openaire +1 more source