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pro vyhledávání: '"Prativadibhayankaram, Srivatsa"'
Autor:
Prativadibhayankaram, Srivatsa, Panda, Mahadev Prasad, Seiler, Jürgen, Richter, Thomas, Sparenberg, Heiko, Fößel, Siegfried, Kaup, André
In this work, we present a comparison between color spaces namely YUV, LAB, RGB and their effect on learned image compression. For this we use the structure and color based learned image codec (SLIC) from our prior work, which consists of two branche
Externí odkaz:
http://arxiv.org/abs/2406.13709
Learned wavelet image and video coding approaches provide an explainable framework with a latent space corresponding to a wavelet decomposition. The wavelet image coder iWave++ achieves state-of-the-art performance and has been employed for various c
Externí odkaz:
http://arxiv.org/abs/2405.12631
Autor:
Prativadibhayankaram, Srivatsa, Panda, Mahadev Prasad, Richter, Thomas, Sparenberg, Heiko, Fößel, Siegfried, Kaup, André
We propose the structure and color based learned image codec (SLIC) in which the task of compression is split into that of luminance and chrominance. The deep learning model is built with a novel multi-scale architecture for Y and UV channels in the
Externí odkaz:
http://arxiv.org/abs/2401.17246
Deep learning based image compression has gained a lot of momentum in recent times. To enable a method that is suitable for image compression and subsequently extended to video compression, we propose a novel deep learning model architecture, where t
Externí odkaz:
http://arxiv.org/abs/2306.17460
In the context of online Robust Principle Component Analysis (RPCA) for the video foreground-background separation, we propose a compressive online RPCA with optical flow that separates recursively a sequence of frames into sparse (foreground) and lo
Externí odkaz:
http://arxiv.org/abs/1710.09160
Autor:
Tescher, Andrew G., Ebrahimi, Touradj, Prativadibhayankaram, Srivatsa, Richter, Thomas, Fößel, Siegfried, Kaup, André
Publikováno v:
Proceedings of SPIE; September 2024, Vol. 13137 Issue: 1 p1313718-1313718-8
