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A Psychological Study: Importance of Contrast and Luminance in Color to Grayscale Mapping

Prasoon Ambalathankandy, Yafei Ou, Sae Kaneko, Masayuki Ikebe

TL;DR

This paper addresses how contrast and luminance influence color-to-grayscale mappings by comparing perceptual-based decolorization methods with spatial-contrast approaches through a psychophysical paired-comparison experiment. It analyzes 54 observers evaluating 53 color images across six grayscale algorithms, using chi-square goodness-of-fit and reaction-time data to distinguish perceptual quality from speeded responses. The findings show that perceptual-based methods, including CIELAB, YCbCr, and WCCD, generally yield higher perceptual quality, while spatial-contrast methods offer faster selections but can introduce artifacts such as artificial contrast and DC-offset. The work provides practical guidance for selecting decolorization techniques based on color variance and application (e.g., video), highlighting the importance of preserving luminance and balancing contrast enhancement for visual fidelity and processing efficiency.

Abstract

Grayscale images are essential in image processing and computer vision tasks. They effectively emphasize luminance and contrast, highlighting important visual features, while also being easily compatible with other algorithms. Moreover, their simplified representation makes them efficient for storage and transmission purposes. While preserving contrast is important for maintaining visual quality, other factors such as preserving information relevant to the specific application or task at hand may be more critical for achieving optimal performance. To evaluate and compare different decolorization algorithms, we designed a psychological experiment. During the experiment, participants were instructed to imagine color images in a hypothetical "colorless world" and select the grayscale image that best resembled their mental visualization. We conducted a comparison between two types of algorithms: (i) perceptual-based simple color space conversion algorithms, and (ii) spatial contrast-based algorithms, including iteration-based methods. Our experimental findings indicate that CIELAB exhibited superior performance on average, providing further evidence for the effectiveness of perception-based decolorization algorithms. On the other hand, the spatial contrast-based algorithms showed relatively poorer performance, possibly due to factors such as DC-offset and artificial contrast generation. However, these algorithms demonstrated shorter selection times. Notably, no single algorithm consistently outperformed the others across all test images. In this paper, we will delve into a comprehensive discussion on the significance of contrast and luminance in color-to-grayscale mapping based on our experimental results and analysis.

A Psychological Study: Importance of Contrast and Luminance in Color to Grayscale Mapping

TL;DR

This paper addresses how contrast and luminance influence color-to-grayscale mappings by comparing perceptual-based decolorization methods with spatial-contrast approaches through a psychophysical paired-comparison experiment. It analyzes 54 observers evaluating 53 color images across six grayscale algorithms, using chi-square goodness-of-fit and reaction-time data to distinguish perceptual quality from speeded responses. The findings show that perceptual-based methods, including CIELAB, YCbCr, and WCCD, generally yield higher perceptual quality, while spatial-contrast methods offer faster selections but can introduce artifacts such as artificial contrast and DC-offset. The work provides practical guidance for selecting decolorization techniques based on color variance and application (e.g., video), highlighting the importance of preserving luminance and balancing contrast enhancement for visual fidelity and processing efficiency.

Abstract

Grayscale images are essential in image processing and computer vision tasks. They effectively emphasize luminance and contrast, highlighting important visual features, while also being easily compatible with other algorithms. Moreover, their simplified representation makes them efficient for storage and transmission purposes. While preserving contrast is important for maintaining visual quality, other factors such as preserving information relevant to the specific application or task at hand may be more critical for achieving optimal performance. To evaluate and compare different decolorization algorithms, we designed a psychological experiment. During the experiment, participants were instructed to imagine color images in a hypothetical "colorless world" and select the grayscale image that best resembled their mental visualization. We conducted a comparison between two types of algorithms: (i) perceptual-based simple color space conversion algorithms, and (ii) spatial contrast-based algorithms, including iteration-based methods. Our experimental findings indicate that CIELAB exhibited superior performance on average, providing further evidence for the effectiveness of perception-based decolorization algorithms. On the other hand, the spatial contrast-based algorithms showed relatively poorer performance, possibly due to factors such as DC-offset and artificial contrast generation. However, these algorithms demonstrated shorter selection times. Notably, no single algorithm consistently outperformed the others across all test images. In this paper, we will delve into a comprehensive discussion on the significance of contrast and luminance in color-to-grayscale mapping based on our experimental results and analysis.
Paper Structure (9 sections, 3 equations, 10 figures)

This paper contains 9 sections, 3 equations, 10 figures.

Table of Contents

  1. Introduction
  2. Related Works
  3. Psychological Experiment, Analysis and Results
  4. Setup, materials & design
  5. Methodology and Hypothesis Testing
  6. Results & discussion
  7. Exploring color variance and video decolorization analysis
  8. Conclusion
  9. Acknowledgment

Figures (10)

  • Figure 1: Evaluation of Decolorization Algorithms: (Top Row) Original Color Images, (Middle Row) Grayscale Images Converted Using a Perceptual Algorithm ambalathankandy2021warm, (Bottom Row) (a) Kim et al. kim2009robust - Demonstrating Loss of Chromatic Contrast. (b) Nafchi et al. nafchi2017corrc2g - Highlighting Mishandling of Variations in Luminance. (c) CIELAB - Illustrating Failure with Isoluminant Colors. (d) Lu et al. lu2014contrast - Displaying Inability to Handle Specific Color Combinations.
  • Figure 2: Our psychological experimental setup: (a) Participant preparing for the test. (b) Sample test display from our experimental dataset showing six grayscales surrounding the color image.
  • Figure 3: Grayscale Conversion Variability [(a) WCCD ambalathankandy2021warm (b) Nafchi et al.nafchi2017corrc2g (c) CIELAB (d) Liu et al.liu2017log (e) Lu et al.lu2014contrast (f) YCbCr]: Top Row - Typical case with a red flower image ${\chi}^2 = 26.77$. Bottom Row - Challenging case with a weather-degraded train image in foggy conditions, ${\chi}^2 = 2.33$, thereby resulting in acceptance of our null hypothesis ($H_{0}$).
  • Figure 4: Participants with quick response times in seconds overwhelmingly preferred spatial contrast-based algorithms. User response times are arranged in ascending order, indicating the speed of decision-making.
  • Figure 5: This visualization offers insights into user preferences and the distribution of output images based on relative RMS contrast. Spatial contrast-based methods enhance contrast in grayscale images. The left Y1-axis represents the curve plot, while the right Y2-axis represents the bar plot.
  • ...and 5 more figures