Color Classified Colorization: Addressing Feature Imbalance in Image Colorization

核心概念

The author addresses the challenge of feature imbalance in image colorization by proposing a method that optimizes class levels and establishes a trade-off between major and minor classes for accurate predictions.

摘要

Automatic colorization of grayscale images with varying colors is challenging due to feature imbalance. The proposed method transforms color values into discrete classes, optimizes class levels, and introduces object-selective color harmonization. Experimental results show superior performance compared to state-of-the-art models.

The study formulates the colorization problem as a classification task.
A weighted function is used to address feature imbalance.
Class optimization and balancing feature distribution are crucial for good performance.
SAM is employed for object-selective color harmonization.
A new evaluation metric, Chromatic Number Ratio (CNR), quantifies color richness.
The proposed model outperforms baselines and SOTA methods in visualization and CNR measurement criteria.

統計資料

During training, we propose a class-weighted function based on true class appearance in each batch.
We propose 215 color classes for the colorization task.
Our model outperforms other models in visualization and CNR measurement criteria.

引述

"We propose a set of formulas to transform continuous double-channel color values into discrete single-channel color classes and vice versa."
"Class levels and feature distribution are fully data-driven."
"Our proposed model outstrips other models in visualization and CNR measurement criteria."

從以下內容提煉的關鍵洞見

CCC

by Mrityunjoy G... 於 arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01476.pdf

深入探究

How does the proposed method handle desaturation issues commonly seen in image colorization

The proposed method addresses desaturation issues in image colorization by optimizing the class levels and implementing a weighted function that assigns higher weights to rarely appearing color classes. This approach ensures that minor colors, which are often desaturated due to their limited appearance in training data, receive adequate attention during the colorization process. By balancing the contribution of all classes through proper weight assignment, the model can overcome desaturation problems and produce more vibrant and accurate colorizations.

What impact does the trade-off between major and minor classes have on the overall performance of the model

The trade-off between major and minor classes plays a crucial role in enhancing the overall performance of the model. By establishing a balance between major (mostly appearing) and minor (rarely appearing) classes, the model can prevent dominant features from overshadowing less common colors. This trade-off helps eliminate biases towards predominant colors while ensuring that all colors have an equal opportunity to be represented accurately in the generated images. As a result, the model can achieve more balanced and realistic color distributions across different objects and backgrounds.

How can the concept of Chromatic Number Ratio be applied to other areas beyond image colorization

The concept of Chromatic Number Ratio (CNR) introduced in image colorization can be applied to other areas beyond visual tasks. For example:

Text Analysis: CNR could be used to quantify diversity or richness of vocabulary usage within text documents.
Music Generation: In music composition, CNR could measure diversity in musical elements like notes, chords, or instruments used.
Biological Data: In genetics research, CNR might assess genetic variation or diversity within populations.
By applying CNR as a metric for quantifying richness or diversity across different domains, researchers can gain insights into patterns and variations present in various datasets or outputs.