COIN: COmpression with Implicit Neural representations

Autor:	Dupont, Emilien, Goliński, Adam, Alizadeh, Milad, Teh, Yee Whye, Doucet, Arnaud
Rok vydání:	2021
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We propose a new simple approach for image compression: instead of storing the RGB values for each pixel of an image, we store the weights of a neural network overfitted to the image. Specifically, to encode an image, we fit it with an MLP which maps pixel locations to RGB values. We then quantize and store the weights of this MLP as a code for the image. To decode the image, we simply evaluate the MLP at every pixel location. We found that this simple approach outperforms JPEG at low bit-rates, even without entropy coding or learning a distribution over weights. While our framework is not yet competitive with state of the art compression methods, we show that it has various attractive properties which could make it a viable alternative to other neural data compression approaches. Comment: Added qualitative comparisons and link to github repo https://github.com/EmilienDupont/coin
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.03123 Zobrazit plný text záznamu View this record from Arxiv