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Digital foresnic analysis for compressed images and videos.
, 2013 The advancement of imaging devices and image manipulation software has made the tasks of tracking and protecting of digital multimedia content becoming increasingly difficult. In order to protect and verify the integrity of the digital content, many active watermarking and passive forensic techniques have been developed for various image and video ...
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Human-Machine Collaborative Image and Video Compression: A Survey
APSIPA Transactions on Signal and Information ProcessingHuanyang Li +4 more
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Neural networks for image and video compression: A review
European Journal of Operational Research, 1998zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christopher Cramer
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Neural compression for hologram images and videos
Optics Letters, 2022Holographic near-eye displays can deliver high-quality three-dimensional (3D) imagery with focus cues. However, the content resolution required to simultaneously support a wide field of view and a sufficiently large eyebox is enormous. The consequent data storage and streaming overheads pose a big challenge for practical virtual and augmented reality ...
Liang Shi +4 more
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IMAGE AND VIDEO COMPRESSION: A REVIEW
International Journal of High Speed Electronics and Systems, 1997The area of image and video compression has made tremendous progress over the last several decades. The successes in image compression are due to advances and better understanding of waveform coding methods which take advantage of the signal statistics, perceptual methods which take advantage of psychovisual properties of the human visual system (HVS)
Christine I. Podilchuk +1 more
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IEEE Potentials, 1998
The authors discuss the underlying principles of image and video compression. The network model they use for image compression is the random neural network (RNN). This pulsed network model provides a somewhat more accurate representation of what occurs in "real" neurons. Signals in the form of pulse trains travel between neurons.
C. Cramer, E. Gelenbe, P. Gelenbe
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The authors discuss the underlying principles of image and video compression. The network model they use for image compression is the random neural network (RNN). This pulsed network model provides a somewhat more accurate representation of what occurs in "real" neurons. Signals in the form of pulse trains travel between neurons.
C. Cramer, E. Gelenbe, P. Gelenbe
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Multi-Plane Image Video Compression
2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), 2020Multiplane Images (MPI) is a new approach for storing volumetric content. MPI represents a 3D scene within a view frustum with typically 32 planes of texture and transparency information per camera. MPI literature to date has been focused on still images but applying MPI to video will require substantial compression in order to be viable for real world
Scott Janus +5 more
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CMOS image sensors with video compression
Proceedings of 1998 Asia and South Pacific Design Automation Conference, 2002This paper describes CMOS image sensors integrating video compression circuits. The on-sensor compression is particularly useful for the low-power design of moving picture compression hardware, which is demanded especially in the mobile computing and telephony.
S. Kawahito +2 more
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Rotational transform for image and video compression
2011 18th IEEE International Conference on Image Processing, 2011To improve video coding efficiency, the Rotational Transform (ROT) was proposed for adaptive switching between different transforms cores. The Karhunen Loeve Transform (KLT) is known to be optimal for given residual but requires much side information to be signaled to the decoder.
Elena Alshina +2 more
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Partial encryption of compressed images and videos
IEEE Transactions on Signal Processing, 2000The increased popularity of multimedia applications places a great demand on efficient data storage and transmission techniques. Network communication, especially over a wireless network, can easily be intercepted and must be protected from eavesdroppers.
Howard Cheng, Xiaobo Li 0001
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