Enhancing Video Colorization with Deep Learning: A Comprehensive Analysis of Training Loss Functions

Leandro Stival*, Ricardo da Silva Torres, Helio Pedrini

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

Abstract

Traditional colorization approaches rely on the expertise of artists or researchers that meticulously paint or digitally add colors to an image (or video frames), which is often a time-consuming, laborious, and error-prone task. Automatic methods, based on deep learning techniques, have replaced such approaches to colorization. Despite the advances toward improving their accuracy, there is no consensus regarding the best training procedures for existing artificial neural network techniques proposed for video colorization. In this paper, in order to fill this gap, we focus on the impact of selecting an appropriate loss function. We investigate seven loss functions to find the combination that gives the best results with Deep Learning Video Colorization (DLVC) using a U-Net topology and an attention mechanism trained on the DAVIS dataset. An investigation of the current validation metrics for colorization results was also conducted to analyze their ability to accurately judge colors between frames.

Original languageEnglish
Title of host publicationIntelligent Systems and Applications
Subtitle of host publicationProceedings of the 2024 Intelligent Systems Conference IntelliSys
EditorsKohei Arai
PublisherSpringer
Pages496-509
Number of pages14
Volume1
ISBN (Electronic)9783031663291
ISBN (Print)9783031663284
DOIs
Publication statusPublished - 2024
EventIntelligent Systems Conference, IntelliSys 2024 - Amsterdam, Netherlands
Duration: 5 Sept 20246 Sept 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1065 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference/symposium

Conference/symposiumIntelligent Systems Conference, IntelliSys 2024
Country/TerritoryNetherlands
CityAmsterdam
Period5/09/246/09/24

Keywords

  • Deep learning
  • Evaluation metrics
  • Training loss function
  • Video colorization

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