Most pixel-value-ordering (PVO) predictors generated prediction-errors including −1 and 1 in a block-by-block manner. Pixel-based PVO (PPVO) method provided a novel pixel scan strategy in a pixel-by-pixel way. Prediction-error bin 0 is expanded for embedding with the help of equalizing context pixels for prediction. In this paper, a PPVO-based hybrid predictor (HPPVO) is proposed as an extension. HPPVO predicts pixel in both positive and negative orientations. Assisted by expansion bins selection technique, this hybrid predictor presents an optimized prediction-error expansion strategy including bin 0. Furthermore, a novel field-biased context pixel selection is already developed, with which detailed correlations of around pixels are better exploited more than equalizing scheme merely. Experiment results show that the proposed HPPVO improves embedding capacity and enhances marked image fidelity. It also outperforms some other state-of-the-art methods of reversible data hiding, especially for moderate and large payloads.

No abstract

No abstract

No abstract

Abstract. The analysis of students' performance in education teaching in colleges and universities plays an important role. However, little attention is paid to these analyses in many universities so far and the existing results of analyses are not comprehensively and thoroughly enough. According to this situation, in order to help students understand their own capabilities and help teachers master students' level, this paper introduced the fuzzy cognitive model and applied it to the course of digital signal processing and other correlated curricula. We analyzed related data with the fuzzy cognitive model in multi-aspects such as the correlation between other courses and digital signal processing, the relationship between prediction results and actual results, the difference between scores of different classes, students' performance trends in different score sections. On these bases, we put forward corresponding teaching suggestions, in order to promote the teachers' teaching level and students' learning effect in the course of digital signal processing.