Directional Coherence-Based Scrolling-Text Detection for Frame Rate Up-Conversion

Abstract

This paper proposes a new scrolling-text detection method that uses directional coherence for frame rate up-conversion (FRUC). Most previous methods use either gradient information or motion vector (MV) distribution of the frame for scrolling-text detection. Edges can be generated by non-text components and the number of MVs to determine the scrolling-text decreases in each row of the frame. Thus, they incorrectly detect the non-text regions as scrolling-text and cannot accurately detect the start or end of text scrolling at the frame boundary. The proposed method overcomes these problems using coherence values of edge directions for each pixel and scrolling-text-aware refinement processes. The key idea of the proposed method is to use the directional coherence of edge directions and use texture patterns analysis-based refinement to improve the accuracy of the scrolling-text detection. For refinement processes, the proposed method extracts texture patterns as bit codes. Then, it computes the diversity of the texture patterns around the detected text edges. In addition, the proposed method extracts the representative value of the MV for the detected region to correct the regions falsely detected as the scrolling-text. With these refinement processes, the proposed method can also accurately detect the start or end of text scrolling at the frame boundary. In the experimental results, the proposed method increased the average F1 score to 0.504 (a 131.25% improvement) compared with previous methods. The average computation time per pixel of the proposed method also decreased to 18.571 μs (an 80.80% reduction) compared with previous methods. INDEX TERMS Frame rate up-conversion, coherence of edge directions, scrolling-text detection, motion estimation.

Luminance Level of Histogram-Based Scene-Change Detection for Frame Rate Up-Conversion

“…Second, we evaluated scene-change detection accuracy for the proposed and benchmark methods. We evaluated scenechange detection accuracy using the most popular evaluation metrics: Precision (P), Recall (R), and F1 score (F1) [17][18][19][20][21]. These evaluation metrics are defined as follows:…”

“…8 (b)). Because ATSCD [9] and HSSCD [11] consider the local scene change, their performance is superior to the previous scene-change detection methods [5], [7], [21]. However, these methods also generate some artifacts in the caption boundary regions (Figs.…”

“…In the second experiment, we compared the accuracy of the proposed and benchmark scene-change detection methods using P, R, and F1 [17][18][19][20][21]. We counted NTP, NFP, and NFN in the video sequences used in our experiments.…”