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Unveiling the Future of Human and Machine Coding: A Survey of End-to-End Learned Image Compression. [PDF]
Entropy (Basel)Huang CH, Wu JL.
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Efficient lossy compression for compressive sensing acquisition of images in compressive sensing imaging systems. [PDF]
Sensors (Basel), 2014 Li X, Lan X, Yang M, Xue J, Zheng N.
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Novel Near-Lossless Compression Algorithm for Medical Sequence Images with Adaptive Block-Based Spatial Prediction. [PDF]
J Digit Imaging, 2016 Song X, Huang Q, Chang S, He J, Wang H.
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Adaptive-compression based congestion control technique for wireless sensor networks. [PDF]
Sensors (Basel), 2010 Lee JH, Jung IB.
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Entropy-Coded Adaptive Differential Pulse-Code Modulation (DPCM) for Speech
IEEE Transactions on Communications, 1974A study of combining two ways of reducing the redundancy in the digital representation of speech signals is presented. Differential pulse-code modulation (DPCM) encodes the signal into digital form and reduces the redundancy due to correlation in adjacent sample values of the signal.
K. Virupaksha, J. O'Neal
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A Unified Representation of Differential Pulse-Code Modulation (DPCM) and Transform Coding Systems
IEEE Transactions on Communications, 1974We consider a transform coding system that uses a lower-triangular transformation to uncorrelate the data. Based on this transformation we propose a generalized differential pulse code modulation (DPCM) system and show that at high bit rates it performs almost as well as coding by the method of principal components (Karhunen-Loeve transformation). This
A. Habibi, R. Hershel
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<title>Predictor Adaptive Differential Pulse Code Modulation (DPCM)</title>
SPIE Proceedings, 1978Hiroshi Yasuda, Hisashi Kawanishi
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