Výsledky vyhledávání

Report

A Study on the Effect of Color Spaces in Learned Image Compression

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

Zobrazit plný text záznamu

Report

SLIC: A Learned Image Codec Using Structure and Color

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

Zobrazit plný text záznamu

Report

Color Learning for Image Compression

Autor: Prativadibhayankaram, Srivatsa, Richter, Thomas, Sparenberg, Heiko, Fößel, Siegfried

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

Zobrazit plný text záznamu