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Dequantenhancement by spatial color algorithms

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Journal of the Optical Society of America A
Authors:
Beatrice Sarti at University of Milan
Beatrice Sarti
Giuliana Ramella at Italian National Research Council
Giuliana Ramella
Alessandro Rizzi at University of Milan
Alessandro Rizzi
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Abstract and Figures

Spatial color algorithms (SCAs) are algorithms grounded in the retinex theory of color sensation that, mimicking the human visual system, perform image enhancement based on the spatial arrangement of the scene. Despite their established role in image enhancement, their potential as dequantizers has never been investigated. Here, we aim to assess the effectiveness of SCAs in addressing the dual objectives of color dequantization and image enhancement at the same time. To this end, we propose the term dequantenhancement. In this paper, through two experiments on a dataset of images, SCAs are evaluated through two distinct pathways: first, quantization followed by filtering to assess both dequantization and enhancement; and second, filtering applied to original images before quantization as further investigation of mainly the dequantization effect. The results are presented both qualitatively, with visual examples, and quantitatively, through metrics including the number of colors, retinal-like subsampling contrast (RSC), and structural similarity index (SSIM).
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Research Article Vol. 41, No. 11 / November 2024 / Journal of the Optical Society of America A 2251
Dequantenhancement by spatial color algorithms
Beatrice Sarti,1Giuliana Ramella,2,*AND Alessandro Rizzi1
1Computer Science Department, Università degli Studi di Milano, Milano, Italy
2C. N. R., National Research Council, Institute for Applications of Calculus “Mauro Picone”, Napoli, Italy
*giuliana.ramella@cnr.it
Received 11 July 2024; revised 5 October 2024; accepted 7 October 2024; posted 8 October 2024; published 31 October 2024
Spatial color algorithms (SCAs) are algorithms grounded in the retinex theory of color sensation that, mimicking
the human visual system, perform image enhancement based on the spatial arrangement of the scene. Despite their
established role in image enhancement, their potential as dequantizers has never been investigated. Here, we aim to
assess the effectiveness of SCAs in addressing the dual objectives of color dequantization and image enhancement
at the same time. To this end, we propose the term dequantenhancement. In this paper, through two experiments
on a dataset of images, SCAs are evaluated through two distinct pathways: first, quantization followed by filtering
to assess both dequantization and enhancement; and second, filtering applied to original images before quantiza-
tion as further investigation of mainly the dequantization effect. The results are presented both qualitatively, with
visual examples, and quantitatively, through metrics including the number of colors, retinal-like subsampling con-
trast (RSC), and structural similarity index (SSIM). © 2024 Optica Publishing Group. All rights, including for text and data
mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.
https://doi.org/10.1364/JOSAA.536515
1. INTRODUCTION
Image enhancement and color quantization are two topics that
have been addressed in the literature separately. Some methods
focus exclusively on resolving color quantization issues without
addressing visual-enhancement-related problems [1], while oth-
ers aim to improve specific visual properties without considering
the effects of the reduction of the number of colors [2]. As far as
we know, there are no works that directly or indirectly address
both aspects.
This work aims to present a novel approach that uses algo-
rithms from the spatial color algorithms (SCAs) family [3]
to achieve both color dequantization and image equalization
at the same time. To reflect this dual purpose, we coined the
term dequantenhancement specifically for the goal of this paper,
which is to present the dequantization properties of this family
of image enhancement algorithms (SCA) based on the color
perception model called “retinex.”
In our vision system, the sensation of color at a specific point
is the result not only of the direct stimulus at that point but
also of the effect of all the other points within the perceived
scene. By mimicking the spatial interpolation process at the base
of human color perception [4], these algorithms increase the
number of colors in an image by recomputing lost tones through
spatial interpolation. While not explicitly designed for color
quantization recovery, this interpolation is the basis for image
enhancement [5].
In this work, we will present several representatives from the
SCA family, without the intention of ranking them. Instead, we
focus on discussing their performance from a dequantenhance-
ment perspective, exploring how the effect of dequantization is
mixed with image enhancement.
2. USED TOOLS
As aforementioned, the experiment aims to evaluate the effec-
tiveness of the SCA algorithms in the dequantenhancement
process, which combines dequantization and image enhance-
ment. Starting from original images, these are first quantized,
reducing the number of colors, and then filtered using SCAs to
recover lost tones and improve overall visual quality. The main
goal is to explore how these algorithms perform color recovery
besides color equalization.
Since the experiment involves two key phases–quantization,
and dequantization through the application of SCA algorithms–
the following subsections will delve into each of these aspects
in detail. We will discuss the quantization methods used, the
approach to dequantization through SCA algorithms, and
the technical details of the implementations adopted for our
experiment.
A. Color Quantization
In image processing (IP), color quantization (CQ) is a method
employed to generate a transformed image with the same spatial
dimensions but characterized by a reduced set of representative
colors and, as possible, visual fidelity to the original.
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