A Proposed Methodology for Evaluating HDR False Color Maps

2016-08-01
Color mapping, which involves assigning colors to the individual elements of an underlying data distribution, is a commonly used method for data visualization. Although color maps are used in many disciplines and for a variety of tasks, in this study we focus on its usage for visualizing luminance maps. Specifically, we ask ourselves the question of how to best visualize a luminance distribution encoded in a high-dynamic-range (HDR) image using false colors such that the resulting visualization is the most descriptive. To this end, we first propose a definition for descriptiveness. We then propose a methodology to evaluate it subjectively. Then, we propose an objective metric that correlates well with the subjective evaluation results. Using this metric, we evaluate several false coloring strategies using a large number of HDR images. Finally, we conduct a second psychophysical experiment using images representing a diverse set of scenes. Our results indicate that the luminance compression method has a significant effect and the commonly used logarithmic compression is inferior to histogram equalization. Furthermore, we find that the default color scale of the Radiance global illumination software consistently performs well when combined with histogram equalization. On the other hand, the commonly used rainbow color scale was found to be inferior. We believe that the proposed methodology is suitable for evaluating future color mapping strategies as well.
ACM TRANSACTIONS ON APPLIED PERCEPTION

Suggestions

A Content-Boosted Collaborative Filtering Approach for Movie Recommendation Based on Local and Global Similarity and Missing Data Prediction
Özbal, Gozde; Karaman, Hilal; Alpaslan, Ferda Nur (Oxford University Press (OUP), 2011-09-01)
Most traditional recommender systems lack accuracy in the case where data used in the recommendation process is sparse. This study addresses the sparsity problem and aims to get rid of it by means of a content-boosted collaborative filtering approach applied to a web-based movie recommendation system. The main motivation is to investigate whether further success can be obtained by combining 'local and global user similarity' and 'effective missing data prediction' approaches, which were previously introduce...
A case study in weather pattern searching using a spatial data warehouse model
Köylü, Çağlar; Akyürek, Sevda Zuhal; Department of Geodetic and Geographical Information Technologies (2008)
Data warehousing and Online Analytical Processing (OLAP) technology has been used to access, visualize and analyze multidimensional, aggregated, and summarized data. Large part of data contains spatial components. Thus, these spatial components convey valuable information and must be included in exploration and analysis phases of a spatial decision support system (SDSS). On the other hand, Geographic Information Systems (GISs) provide a wide range of tools to analyze spatial phenomena and therefore must be ...
A unifying grid approach for solving potential flows applicable to structured and unstructured grid configurations
Cete, A. Ruhsen; Yuekselen, M. Adil; Kaynak, Uenver (Elsevier BV, 2008-01-01)
In this study, an efficient numerical method is proposed for unifying the structured and unstructured grid approaches for solving the potential flows. The new method, named as the "alternating cell directions implicit - ACDI", solves for the structured and unstructured grid configurations equally well. The new method in effect applies a line implicit method similar to the Line Gauss Seidel scheme for complex unstructured grids including mixed type quadrilateral and triangle cells. To this end, designated al...
A pattern classification approach for boosting with genetic algorithms
Yalabık, Ismet; Yarman Vural, Fatoş Tunay; Üçoluk, Göktürk; Şehitoğlu, Onur Tolga (2007-11-09)
Ensemble learning is a multiple-classifier machine learning approach which produces collections and ensembles statistical classifiers to build up more accurate classifier than the individual classifiers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this study, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed system finds an elegant way of boosting a bunch of classifiers successively ...
A clustering method for web data with multi-type interrelated components
Bolelli, Levent; Ertekin Bolelli, Şeyda; Zhou, Ding; Giles, C Lee (2007-05-08)
Traditional clustering algorithms work on "flat" data, making the assumption that the data instances can only be represented by a set of homogeneous and uniform features. Many real world data, however, is heterogeneous in nature, comprising of multiple types of interrelated components. We present a clustering algorithm, K-SVMeans, that integrates the well known K-Means clustering with the highly popular Support Vector Machines(SVM) in order to utilize the richness of data. Our experimental results on author...
Citation Formats
A. O. Akyüz, “A Proposed Methodology for Evaluating HDR False Color Maps,” ACM TRANSACTIONS ON APPLIED PERCEPTION, pp. 0–0, 2016, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/38674.