Efficient scrolling videotext detection with adaptive temporal differential approach

Hsia, Shih-Chang; Chang-Jian, Nan-Tsai

doi:10.1049/iet-ipr.2013.0448

Cited by 4 publications

(12 citation statements)

References 19 publications

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“…In the first experiment, we visually compared the quality of the interpolated frames generated by conventional FRUC [15] using the previous and proposed methods (Figs 7-8). TSTD [21], GSTD [22], HSTD [23], and KSTD [24] could not detect the scrolling-text starting at the frame boundary. Hence, because these methods cannot correct incorrectly-estimated MVs in the scrolling-text regions, they generate severe artifacts at the scrolling-text region in the interpolated frames during FRUC.…”

Section: Test Sequences (Number Of Frames)mentioning

confidence: 90%

“…It detects the final scrolling-text regions by comparing the motion direction of the candidate regions with the typical direction in which scrolling-text flows (horizontal or vertical). This method demonstrated improved the performance of the scrolling-text detection compared with previous methods [21]- [23]. However, this method is insufficient for detecting text edges because text edges exist that do not satisfy the new hypothesis used in this method [24].…”

Section: Related Workmentioning

confidence: 91%

“…TSTD [21] GSTD [22] HSTD [23] KSTD [24] LSTD [25] Proposed method P R F1 P R F1 P R F1 P R F1 P R F1 P R the proposed and the previous scrolling-text detection methods. First, we visually evaluated the quality of the interpolated frames generated by FRUC using the proposed and the previous scrolling-text detection methods.…”

Section: Test Sequences (Number Of Frames)mentioning

confidence: 99%

“…An adaptive temporal differential-based detection method [23] has been proposed to improve the detection accuracy of scrolling-text detection. It calculates the difference between the edge map of the previous and current frames obtained from a Roberts edge detector [13].…”

Section: Related Workmentioning

confidence: 99%

“…Third, we measured the computation times of the proposed and previous scrolling-text detection methods. As the performance of previous methods to compare the performance of the proposed method, we used five previous methods: Tsai's scrolling-text detection (TSTD) [21], Gim's scrolling-text detection (GSTD) [22], Hsia's scrolling-text detection (HSTD) [23], Khare's scrolling-text detection (KSTD) [24], and Lee's scrolling-text detection (LSTD) [25]. LSTD is the most state-of-the-art scrolling-text detection method.…”

Section: Test Sequences (Number Of Frames)mentioning

confidence: 99%

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

Lee

Cho

2020

IEEE Access

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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.

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