| Paper ID | MMSP-4.5 |
| Paper Title |
IMAGE CODING WITH NEURAL NETWORK-BASED COLORIZATION |
| Authors |
Diogo Lopes, João Ascenso, Catarina Brites, Fernando Pereira, Instituto Superior Técnico, Universidade de Lisboa - Instituto de Telecomunicações, Portugal |
| Session | MMSP-4: Image, Video and Point Cloud Coding |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation Time: | Wednesday, 09 June, 14:00 - 14:45 |
| Presentation |
Poster
|
| Topic |
Multimedia Signal Processing: Signal Processing for Multimedia Applications |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Automatic colorization is a process with the objective of inferring the color of grayscale images. This process is frequently used for artistic purposes and to restore the color in old or damaged images. Motivated by the excellent results obtained with deep learning-based solutions in the area of automatic colorization, this paper proposes an image coding solution integrating a deep learning-based colorization process to estimate the chrominance components based on the decoded luminance which is regularly encoded with a conventional image coding standard. In this case, the chrominance components are not coded and transmitted as usual, notably after some subsampling, as only some color hints, i.e. chrominance values for specific pixel locations, may be sent to the decoder to help it creating more accurate colorizations. To boost the colorization and final compression performance, intelligent ways to select the color hints are proposed. Experimental results show performance improvements with the increased level of intelligence in the color hints extraction process and a good subjective quality of the final decoded (and colorized) images. |
