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

Abstract

Scene change detection is an essential process of frame rate up-conversion (FRUC). The performance of FRUC highly dependents on the accuracy of scene change detection. This paper proposes a new scene-change detection method that uses analysis of luminance level of the histograms for FRUC. The histogram luminance level refers to the statistical average luminance value obtained from the generated histograms for each region. Existing histogram-based scene change methods calculate the difference between optimal threshold values using an automatic thresholding technique or extract the difference between the histogram shape to detect the scene change. The automatic thresholding method uses iterative operations—the difference between the histogram shape is simply a method of calculating the luminance difference for the current and previous frames. Thus, it requires many computational resources and incorrectly detects a scene change because calculating the histogram shape cannot reflect regional image characteristics. The proposed method addresses these problems using histogram luminance levels for each region in the given frames. It calculates the level differences between the previous and current frames to detect the initial scene change regions. Moreover, the proposed method refines the initial scene change regions by analyzing the distribution of surrounding detected regions and uses refinement to enhance scene-change detection accuracy. In the experimental results, the proposed method increased the average F1 score to 0.4816 (a 122.51% improvement) compared with the benchmark methods. The average computation time per pixel of the proposed method also decreased to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.5323 \,\,\mu \text{s}$ </tex-math></inline-formula> (a 87.06% reduction) compared with the benchmark methods.