다양한 이미지 샘플의 Eye test를 바탕으로 기술적인 화질 지표 조절을 통하여 감성 화질을 최적화 시키는 방법이 소개된다. 여러 가지 이미지 콘텐츠의 다양한 이미지에 대하여, 콘트라스트, 명도, 채도 화질 지표 톤 커브를 사용하여 평가가 수행 되었다. 이미지 화질 향상에 기여하는 우선순위는 명암, 채도 및 밝기 순으로 분석 되었다. 이미지 감성 화질 측정치의 기술적인 화질 지표 변화에 따른 기울기의 공통점을 살펴본 결과, 거의 0, 중간 그리고 최대 기울기의 영역으로 구성된 함수 형태로 모델링을 할 경우, 기존의 역 U 형태의 성질 뿐 아니라 log 또는 포화 형태의 감성 화질 변화까지 분석 가능함을 알 수 있었다. 단일 및 복수의 화질 지표의 경우에 대하여도 화질 개선 방안이 모색 되었으며, 기존 및 본 논문에서 분석된 결과를 위한 새로운 함수가 소개 되었다. 복수의 통합적 이미지 화질 지표를 통하여 향상 시킬 경우 오직 몇몇 한정된 지표 제어의 경우에만 실현 가능하다는 것을 알 수 있었다. 또한, 화질 향상 방법은 영상 콘텐츠에 따라 크게 차이가 없음을 알 수 있었다.

This research discusses the characteristics and the implementation strategies for two types of quality metrics to analyze innovation effects in six sigma projects: fixed specification type and moving specification type. Zst, Ppk are quality metrics of fixed specification type that are influenced by predetermined specification. In contrast, the quality metrics of moving specification type such as Strictly Standardized Mean Difference(SSMD), Z-Score, F-Statistic and t-Statistic are independent from predetermined specification. Zst sigma level obtains defective rates of Parts Per Million(PPM) and Defects Per Million Opportunities(DPMO). However, the defective rates between different industrial sectors are incomparable due to their own technological inherence. In order to explore relative method to compare defective rates between different industrial sectors, the ratio of specification and natural tolerance called, Ppk, is used. The drawback of this Ppk metric is that it is highly dependent on the specification. The metrics of F-Statistic and t-Statistic identify innovation effect by comparing before-and-after of accuracy and precision. These statistics are not affected by specification, but affected by type of statistical distribution models and sample size. Hence, statistical significance determined by above two statistics cannot give a same conclusion as practical significance. In conclusion, SSMD and Z-Score are the best quality metrics that are uninfluenced by fixed specification, theoretical distribution model and arbitrary sample size. Those metrics also identify the innovation effects for before-and-after of accuracy and precision. It is beneficial to use SSMD and Z-Score methods along with popular methods of Zst sigma level and Ppk that are commonly employed in six sigma projects. The case studies from national six sigma contest from 2011 to 2012 are proposed and analyzed to provide the guidelines for the usage of quality metrics for quality practitioners.

This research paper introduces the application and implementation of medical decision metrics that classifies medical decision-making into four different metrics using statistical diagnostic tools, such as confusion matrix, normal distribution, Bayesian prediction and Receiver Operating Curve(ROC). In this study, the metrics are developed based on cross-section study, cohort study and case-control study done by systematic literature review and reformulated the structure of type I error, type II error, confidence level and power of detection. The study proposed implementation strategies for 10 quality improvement activities via 14 medical decision metrics which consider specificity and sensitivity in terms of α and β. Examples of ROC implication are depicted in this paper with a useful guidelines to implement a continuous quality improvement, not only in a variable acceptance sampling in Quality Control(QC) but also in a supplier grading score chart in Supplier Chain Management(SCM) quality. This research paper is the first to apply and implement medical decision-making tools as quality improvement activities. These proposed models will help quality practitioners to enhance the process and product quality level.