Existing Complexity Metrics Research Articles

Dual-complexities based on straightforward neighborhood pixel prediction in pixel-value-ordering framework for reversible data hiding

PVO-based schemes are the widely-used reversible data hiding (RDH) techniques. Benefiting from the good prediction performance, the stego-image can have a high quality. However, the complexity metric of PVO is still not good enough. The two main limitations are: the block-based context pixels are not highly correlated with the predicted pixel, and the fluctuation-based complexity calculation methods cannot comprehensively represent the real prediction result. Unlike the existing complexity metrics, we consider this problem from a novel viewpoint of neighborhood pixel prediction (NPP), i.e., using the prediction pixel to predict the unmodified neighborhood pixels of a predicted pixel. The neighborhood pixels are more reliable than the context pixels and the generated neighborhood prediction-errors (NPEs) are utilized to represent the real prediction-error (RPE). Two new features are extracted from NPEs as Dual-complexities to determine the embedding order. Experimental results indicate the quality of the stego-image can be improved significantly by using our proposed Dual-complexities in the related PVO-based schemes, and it can be directly extended to other schemes in PVO framework as well.

Minimum description length clustering to measure meaningful image complexity

We present a new image complexity metric. Existing complexity metrics cannot distinguish meaningful content from noise, and give a high score to white noise images, which contain no meaningful information. We use the minimum description length principle to determine the number of clusters and designate certain points as outliers and, hence, correctly assign white noise a low score. The presented method is a step towards humans’ ability to detect when data contain a meaningful pattern. It also has similarities to theoretical ideas for measuring meaningful complexity. We conduct experiments on seven different sets of images, which show that our method assigns the most accurate scores to all images considered. Additionally, comparing the different levels of the hierarchy of clusters can reveal how complexity manifests at different scales, from local detail to global structure. We then present ablation studies showing the contribution of the components of our method, and that it continues to assign reasonable scores when the inputs are modified in certain ways, including the addition of Gaussian noise and the lowering of the resolution. Code is available at https://github.com/Lou1sM/meaningful_image_complexity.

Another New Complexity Metric for Object-Oriented Design Measurement

This paper presents a new complexity metric for Object-Oriented (OO) design measurement to find at the design stage whether the classes become more complex, Moderate complex or less complex. The proposed metric is theoretically evaluated against the Weyuker’s properties as well as empirically evaluated against three open source software system. Furthermore, for validating the validity of new complexity metric, the paper has also presented the comparison of proposed metric with some well known complexity metrics like Weighted Method per Class (WMC) metric of Chidamber and Kemerer (CK), Class Complexity (CC) metric of Balasubramanian, and Complexity Metric for OO Design (CMOOD) metric of Rajnish and Bhattacherjee (RB) against the same three open source software system..Automated tool were used to generate the metric values and for analyzing the results, IBM SPSS software used. The results in this paper indicates that the new complexity metric is correlated well with existing complexity metrics and may be used as predictors of complexity of class.

유사도 기반의 웹 어플리케이션 구조 복잡도

Abstract Software complexity is used to evaluate a target system's maint ainability. The existing complexity metrics on web applications are count-based, so it is hard to incorporate the understandability of developers or maintainers. To make up for this shortcomings, entropy-theory can be applied to define complexity, however, it is assumed that information quantity of each paper is identical. In this paper, structural complexity of a w eb application is defined based on information theory and similarity. In detail, the proposed comp lexity is defined using entropy as the previous approach, but the information quantity of individu al pages is defined using similarity. That is, a page which are similar with many pages has smaller i nformation quantity than a page which are dissimilar to others. Furthermore, various similarity measures can be used for various views, which results in many-sided complexity measures. Finally , several complexity properties are applied to verify the proposed metric and case studies shows th e applicability of the metric.▸Keyword:웹 어플리케이션(web application), 복잡도(complexity), 유사도(similar ity)∙제1저자 : 정우성 교신저자 : 이은주∙투고일 : 2010. 05. 02, 심사일 : 2010. 05. 18, 게재확정일 : 2010. 05. 30.* 서울대학교 공과대핟 컴퓨터공학부 박사과정 ** 경북대학교 IT대학 컴퓨터학부 조교수※ 이 논문은 2007년도 정부(교육과학기술부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업 연구임(NRF-2007-331-D00407)

Two dimensions of program complexity

Program comprehension is viewed as a two-stage process; the two stages broadly consists of formation and use of a mental representation of the program. Control and execution structures of the program are expected to play a dominant role in the first stage, i.e. formation of the mental representation. This article reports results of an experiment to study the effect of control and execution structures on program comprehension. Results point to certain limitation of existing complexity metrics.