M
Marco Grangetto
Researcher at University of Turin
Publications - 194
Citations - 3010
Marco Grangetto is an academic researcher from University of Turin. The author has contributed to research in topics: Network packet & Error detection and correction. The author has an hindex of 26, co-authored 194 publications receiving 2660 citations. Previous affiliations of Marco Grangetto include Polytechnic University of Turin & Xi'an Jiaotong-Liverpool University.
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Multimedia Selective Encryption by Means of Randomized Arithmetic Coding
TL;DR: A randomized arithmetic coding paradigm is introduced, which achieves encryption by inserting some randomization in the arithmetic coding procedure, and unlike previous works on encryption by arithmetic coding, this is done at no expense in terms of coding efficiency.
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Unveiling COVID-19 from CHEST X-Ray with Deep Learning: A Hurdles Race with Small Data
TL;DR: In this paper, the authors provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images and provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets.
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Redundant Slice Optimal Allocation for H.264 Multiple Description Coding
TL;DR: This paper addresses the creation of two balanced descriptions based on the concept of redundant slices, while keeping full compatibility with the H.264 standard syntax and decoding behavior in case of single description reception.
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Sliding-Window Raptor Codes for Efficient Scalable Wireless Video Broadcasting With Unequal Loss Protection
TL;DR: This paper introduces a new class of digital fountain codes based on a sliding-window approach applied to Raptor codes, which have several properties useful for video applications, and provide better performance than classical digital fountains.
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Concealment of whole-frame losses for wireless low bit-rate video based on multiframe optical flow estimation
TL;DR: Results show that the proposed algorithm significantly outperforms other techniques by several dBs in peak signal-to-noise ratio (PSNR), provides good visual quality, and has a rather low complexity, which makes it possible to perform real-time operation with reasonable computational resources.