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Related Experiment Video

Updated: Jul 17, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Example-based color transformation of image and video using basic color categories.

Youngha Chang¹, Suguru Saito, Masayuki Nakajima

¹Department of Computer Science, Tokyo Institute of Technology, W8-64, 2-12-1 O-Okayama, Meguroku, Japan. chang@img.cs.titech.ac.jp

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|February 3, 2007

Summary

This study enhances image and video color stylization for improved mood. The improved method offers more natural results with less manual effort, making color transformation more accessible.

More Related Videos

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Related Experiment Videos

Last Updated: Jul 17, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

Computer Vision
Digital Image Processing
Computational Aesthetics

Background:

Color significantly influences perceived mood in images.
Conventional color transformation tools often involve a trade-off between image quality and manual effort.
Previous work introduced example-based color stylization using perceptual color categories.

Purpose of the Study:

To extend existing example-based color stylization methods for greater robustness.
To enable natural and detailed color stylization for video frame sequences.
To explore the impact of color transformation on conveying coherent moods in visual media.

Main Methods:

Developing a more robust algorithm for example-based color stylization.
Adapting the method to process and stylize video frame sequences.
Utilizing perceptual color categories for example-based transformations.

Main Results:

The enhanced algorithm successfully stylizes colors in video sequences.
The method produces detailed and natural-looking results with reduced manual operation.
Demonstrated ability to convey distinct yet coherent moods through color transformation in images and videos.

Conclusions:

The extended method provides a more robust and efficient approach to image and video color stylization.
This technique offers a powerful tool for mood manipulation in visual content.
The findings support the significant role of color in establishing and altering the emotional tone of visual media.