Experimental study on perception of color encoding based on information interface statistical task

doi:10.11996/JG.j.2095-302X.2023020408

Abstract

Abstract:

In order to effectively enhance users′ decision-making efficiency for color encoding in visual statistics tasks, it is necessary to first investigate the perceptual problem of the human visual system for color encoding in visualization interfaces. Therefore, the concept of significant difference was introduced into the color encoding research, leading to the creation of a set of new color spaces for visual statistics tasks. The effect of color difference threshold on the perceptual performance of visual statistics tasks was then examined through typical visual statistics task experiments, so as to summarize the perceptual accuracy and laws of color coding for visual statistics tasks. The results revealed that: firstly, in the correlation recognition task and the mean comparison task, the perceptual accuracy of the human visual system was more accurate than that predicted by previous color models, indicating that the difference threshold (JND) selection for color encoding could be smaller. Secondly, when different color axes were selected for color encoding, the recognition performance of the correlation recognition task was higher with lightness encoding. Finally, under different basic correlation coefficients and number of point groups, the accuracy of correlation recognition and mean comparison followed the Weber linear function, which reflected the observers′ ability to process information entropy when performing visual statistical tasks. The above findings provide design guidance for information visualization designers, and offer a quantitative basis for color coding of visual interface information from the perspective of psychophysics.

Key words: information interface, visual statistical task, color encoding, color perception, interface evaluation

CLC Number:

TP47

GUO Qi, WU Jin-chun, XUE Cheng-qi. Experimental study on perception of color encoding based on information interface statistical task[J]. Journal of Graphics, 2023, 44(2): 408-414.

Figures/Tables 11

Fig. 1 Display step step from the target color to the color space

Fig. 2 Target color summary

Fig. 3 Schematic of experimental material for correlation experimental task and mean comparison task

Table 1 LSD statistics results from post hoc multiple tests

比较项	标准误差	F检验统计量	显著性 P值
1ND-2ND	0.007	3 102	0.113
1ND-无干扰	0.008	3 102	0.000
2ND-无干扰	0.008	3 102	0.000

Table 2 Tukey HSD statistical results of post-hoc multiple tests

比较项	评估值	标准误差	统计	显著性P值
H-S	0.003	0.008	0.283	0.771
L-H	0.022	0.008	3.368	0.003
L-S	0.015	0.008	2.921	0.021

Fig. 4 Results of correlation identification under different color spatial distances

Fig. 5 Results of identifying correlations across different color axes

Fig. 6 Judgment results of centroid difference at different color space distances

Fig. 7 Judgment result of the centroid difference of the different color axes

Fig. 8 A command and combat system interface (simplified version)

Table 3 Basic statistical representation of the interface reaction time

类别	样本容量	反应时均值	标准差	标准误差
1ND	21	417.69	320.07	74.19
2ND	21	373.43	312.89	71.33

References 22

[1]	NAEEM M, JAMAL T, DIAZ-MARTINEZ J, et al. Trends and future perspective challenges in big data[M]// Advances in Intelligent Data Analysis and Applications. Singapore: Springer Singapore, 2021: 309-325.
[2]	GIORGIANNI E J, MADDEN T E. Digital color management: encoding solutions[M]. United States: Addison-Wesley Longman Publishing, 1998: 1-576.
[3]	PECHO O E, GHINEA R, ALESSANDRETTI R, et al. Visual and instrumental shade matching using CIELAB and CIEDE2000 color difference formulas[J]. Dental Materials, 2016, 32(1): 82-92. DOI PMID
[4]	LUO M, POINTER M. CIE colour appearance models: a current perspective[J]. Lighting Research & Technology, 2018, 50(1): 129-140.
[5]	SEYMOUR J. Color inconstancy in CIELAB: a red herring?[J]. Color Research & Application, 2022, 47(4): 900-919.
[6]	SZAFIR D A. Modeling color difference for visualization design[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1): 392-401. DOI URL
[7]	KIM Y, HEER J. Assessing effects of task and data distribution on the effectiveness of visual encodings[J]. Computer Graphics Forum, 2018, 37(3): 157-167.
[8]	SEVA R, CHINJEN K, ESTOISTA N, et al. Indicator distance and color effects in comprehension of multiple time series graph[C]// The International Conference on Industrial Engineering and Operations Management. Nottingham: IEOM Press, 2020: 531-538.
[9]	SCHLOSS K B, LESSARD L, RACEY C, et al. Modeling color preference using color space metrics[J]. Vision Research, 2018, 151: 99-116. DOI PMID
[10]	WANG Y, CHEN X, GE T, et al. Optimizing color assignment for perception of class separability in multiclass scatterplots[J]. IEEE Transactions on Visualization and Computer Graphics, 2019, 25(1): 820-829. DOI URL
[11]	ZHENG Q, LU M, WU S C, et al. Image-guided color mapping for categorical data visualization[J]. Computational Visual Media, 2022, 8(4): 613-629. DOI
[12]	郭琪, 薛澄岐, 周小舟, 等. 识别任务驱动的可视化界面颜色编码感知量化实验研究[J]. 东南大学学报: 自然科学版, 2019, 49(6): 1048-1053.
	GUO Q, XUE C Q, ZHOU X Z, et al. Experimental study on quantization perception of color coding based on visualization interface recognition task[J]. Journal of Southeast University: Natural Science Edition, 2019, 49(6): 1048-1053. (in Chinese)
[13]	TAKAHASHI K, HIRANO G, HASEGAWA T, et al. The weber-Fechner law[J]. Siauliai Mathematical Seminar, 2011(14): 85-91.
[14]	BRILL M H. Is CIELAB one space or many?[J]. Coloration Technology, 2021, 137(1): 83-85. DOI URL
[15]	MAJI S, DINGLIANA J. Perceptually optimized color selection for visualization[EB/OL]. [2022-05-26]. https://arxiv.org/abs/2205.14472.
[16]	PETROFF M A. Accessible color sequences for data visualization[EB/OL]. [2022-05-26]. https://arxiv.org/abs/2107.02270.
[17]	SZAFIR D A. Modeling color difference for visualization design[J]. IEEE Transactions on Visualization and Computer Graphics, 2018, 24(1): 392-401. DOI URL
[18]	CSIKSZENTMIHALYI M, LARSON R. Validity and reliability of the experience-sampling method[J]. Foundations of Positive Psychology, 1987, 175(9): 526-536.
[19]	HAVILAND J, THOMASON A. On testing the ‘pseudo- randomness’ of a hypergraph[J]. Discrete Mathematics, 1992, 103(3): 321-327. DOI URL
[20]	RENSINK R A, BALDRIDGE G. The perception of correlation in scatterplots[J]. Computer Graphics Forum, 2010, 29(3): 1203-1210. DOI URL
[21]	KUBOTA Y, HAYAKAWA T, FUKAYAMA O, et al. Sequential estimation of psychophysical parameters based on the paired comparison[C]// 2022 IEEE/SICE International Symposium on System Integration. New York: IEEE Press, 2022: 150-154.
[22]	赵夫群, 周明全. 改进的概率迭代最近点配准算法[J]. 图学学报, 2017, 38(1): 15-22.
	ZHAO F Q, ZHOU M Q. Improved probability iterative closest point registration algorithm[J]. Journal of Graphics, 2017, 38(1): 15-22. (in Chinese)