Publication Type

Journal Article

Version

publishedVersion

Publication Date

8-2021

Abstract

Color dimensionality reduction is believed as a non-invertible process, as re-colorization results in perceptually noticeable and unrecoverable distortion. In this article, we propose to convert a color image into a grayscale image that can fully recover its original colors, and more importantly, the encoded information is discriminative and sparse, which saves storage capacity. Particularly, we design an invertible deep neural network for color encoding and decoding purposes. This network learns to generate a residual image that encodes color information, and it is then combined with a base grayscale image for color recovering. In this way, the non-differentiable compression process (e.g., JPEG) of the base grayscale image can be integrated into the network in an end-to-end manner. To further reduce the size of the residual image, we present a specific layer to enhance Sparsity Enforcing Priors (SEP), thus leading to negligible storage space. The proposed method allows color embedding on a sparse residual image while keeping a high, 35dB PSNR on average. Extensive experiments demonstrate that the proposed method outperforms state-of-the-arts in terms of image quality and tolerability to compression.

Keywords

Decolorization, colorization, sparsity enforcing priors, convolutional neural networks

Discipline

Graphics and Human Computer Interfaces

Research Areas

Software and Cyber-Physical Systems

Publication

ACM Transactions on Multimedia Computing, Communications and Applications

Volume

17

Issue

3

First Page

1

Last Page

17

ISSN

1551-6857

Identifier

10.1145/3451993

Publisher

Association for Computing Machinery (ACM)

Copyright Owner and License

Publisher

Additional URL

https://doi.org/10.1145/3451993

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