WO2016037422A1 - Procédé de détection d'un changement d'une scène vidéo - Google Patents

Procédé de détection d'un changement d'une scène vidéo Download PDF

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WO2016037422A1
WO2016037422A1 PCT/CN2014/092640 CN2014092640W WO2016037422A1 WO 2016037422 A1 WO2016037422 A1 WO 2016037422A1 CN 2014092640 W CN2014092640 W CN 2014092640W WO 2016037422 A1 WO2016037422 A1 WO 2016037422A1
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video
histogram
threshold
hue
pixel
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PCT/CN2014/092640
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English (en)
Chinese (zh)
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刘鹏
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刘鹏
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N5/00—Details of television systems
    • H04N5/14—Picture signal circuitry for video frequency region

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  • the present invention relates to video image analysis technology, and in particular to a method for detecting video scene changes.
  • the content type of the video is also different during the playback of the video.
  • the type conversion of the video often occurs at the moment of the video scene change.
  • the scene change of the video often causes the content type of the video to change.
  • Existing video scene detection methods mainly include:
  • Chinese patent application CN201310332133.4 proposes a dynamic video scene change detection method, comprising the steps of: acquiring a current frame of a dynamic video image in real time; calculating a scene transformation feature parameter ti(n) of the current frame; and according to the dynamic video image
  • the scene transformation feature parameter of the previous one or several frames is calculated corresponding to the dynamic threshold threshold(n) of the current frame; determining whether the scene transformation feature parameter ti(n) of the current frame is less than or equal to its corresponding dynamic threshold, and if so, Then, it is determined that it is not a scene change frame; otherwise, it is determined to be a scene change frame.
  • a scene detection method based on an undirected weighted graph.
  • the method treats all video sequences as the endpoints of the image, uses the similarity of the video sequence in the spatial and temporal domains as the distance between each edge, and loops through the end points of the graph in a tree stripping manner, each time determining a most likely scene. Boundary until the end of the graph The points are all stripped.
  • the existing detection methods can detect the change of the video scene
  • the existing video scene change detection method has the defects of complicated processing and low detection efficiency.
  • the present invention provides a video scene change detection method that is simple to implement and fast to detect.
  • the invention adopts the following technical solutions: a method for detecting a video scene change, which comprises the steps of:
  • the method for detecting a video scene change further includes the step of performing pixel preprocessing on the image frame of the video file before the step B.
  • the step of preprocessing the pixel specifically includes:
  • the saturation S of a certain pixel point is less than the preset first threshold T1 and the brightness V of the pixel is less than the preset second threshold T2, the pixel is discarded;
  • the remaining pixels in the image frame of the video sequence are preserved.
  • the fourth threshold T4 0.2.
  • the histograms of the hue H components of each image frame are superimposed and their mean values are respectively taken, and the histograms of the average hue of each video sequence are respectively calculated.
  • the step C specifically includes:
  • the total number of pixels counted by the histogram of the hue tone is calculated
  • the normalized histogram of the histogram of the histogram is divided by the number of pixels of each field of the hue histogram divided by the total number of pixels.
  • the present invention has the following beneficial effects:
  • the invention provides a scene detection method based on a histogram of histograms, firstly determining the main color of the background color of the video sequence according to the cumulative histogram corresponding to the hue components of the color categories of each video sequence, according to the adjacent video sequence.
  • the main difference in hue between the video sequences enables fast video scene detection.
  • the invention can also be further applied to other fields of image detection, and has high application value.
  • Figure 1 is a flow chart showing an embodiment of the present invention.
  • the present invention expresses colors according to each video sequence.
  • the cumulative histogram corresponding to the hue component of the category determines the main hue of the background color of the video sequence, and fast video scene detection is implemented on the basis of the video sequence according to the main hue difference between adjacent video sequences.
  • a preferred embodiment of the present invention includes the following implementation steps:
  • Step S1 Convert the image frame of the video file from the RGB space to the HSV space.
  • the RGB color model is usually used, which adopts the three primary color mechanism of color. Although it has a very clear physical meaning, it is not suitable for human visual features.
  • the HSV color model is more suitable for human visual features.
  • the HSV color model determines one color using three parameters: hue H (Hue), saturation S (Saturation), and brightness V (Value).
  • hue H Hue
  • saturation S saturation
  • brightness V Value
  • the color type represented by the hue H can directly reflect the color values of the corresponding wavelengths in the color and the spectrum, such as red, orange, yellow, green, blue, purple, etc.
  • the saturation S represents the vividness of the color, which can be understood as a certain
  • the proportion of the white component in the color the larger the S, the less the white component, the brighter the color
  • the brightness V represents the degree of lightness and darkness of the color, and there is no direct relationship between the light intensity and the light intensity.
  • Step S2 performing pixel preprocessing on the image frame of the video file.
  • each image frame of the pre-video sequence needs to be pre-processed to filter out pixels whose colors can be recognized by the human eye.
  • the pixel pre-processing process determines whether a pixel point can be recognized by setting a certain threshold value for the saturation S and the brightness V: when the saturation S of a certain pixel point is smaller than a preset first threshold value T1 and the pixel point is When the brightness V is less than the preset second threshold T2, the pixel point is discarded; when the saturation S of a certain pixel point is greater than the preset third threshold T3 and the brightness V of the pixel is less than the preset fourth threshold T4, Pixels are discarded; the remaining pixels in the image frame of the video sequence are preserved.
  • Step S3 Dividing the video file into a plurality of video sequences, and calculating a perforated histogram of each video sequence.
  • the average hue histogram refers to the cumulative average histogram of the H component of the video sequence. It counts the total number of pixels corresponding to each tone level of a multi-frame image within a certain range.
  • the average histogram can also be regarded as A histogram of the H component is obtained for all pixels of a video.
  • the entire video sequence (or video file) is divided into a plurality of video sequences by a predetermined length, and each video sequence includes N image frames. Therefore, if you want to detect faster, you can choose a larger N value. If the detection result is more accurate, you can choose a relatively small N value.
  • the perforation histogram L m (K) of the video sequence can be expressed as the following formula (4):
  • a video sequence containing N image frames actually calculates the hue H component histogram of each image frame as H n (K) and then takes the mean value after superposition, and obtains the average color tone of the video sequence.
  • Figure L m (K)
  • step S4 the averaging hue histogram is normalized.
  • step S2 After the image frame is preprocessed in step S2, the number of remaining pixels in each image frame is also different, which causes the total number of statistical pixel points of the perforation histogram of each video sequence to be different. Therefore, it is necessary to normalize the histogram of the hue tone of each video sequence to facilitate comparison of the hue histogram between each video sequence.
  • the present invention employs a normalization process based on total pixel points. After obtaining the corresponding histogram of the hue of the video sequence, the total number of pixels counted by the histogram of the average hue of the field is calculated, and then the number of pixels H(K) of the field histogram is divided by the pixel point. The number of totals is the normalized histogram of the histogram of the histogram.
  • Step S5 Perform matching calculation on the average hue histogram of the adjacent two video sequences to obtain a matching coefficient ⁇ .
  • the distribution of the permeation histogram H1 represented by the matching coefficient ⁇ deviates from the distribution of the histogram H2 of the averaging hue, and the smaller the matching coefficient ⁇ indicates that the lower the degree of deviation, the more the two histograms H1 and H2 match.
  • Step S6 sequentially determining whether the matching coefficient ⁇ between the histograms of the adjacent two video sequences is greater than a preset matching threshold. If yes, the adjacent two video sequences are considered to be video sequences of different scenes, otherwise Is a video sequence of the same scene.
  • the invention provides a scene detection method based on a histogram of histograms, firstly determining the main color of the background color of the video sequence according to the cumulative histogram corresponding to the hue components of the color categories of each video sequence, according to the adjacent video sequence.
  • the main difference in hue between the video sequences enables fast video scene detection.
  • the invention can also be further applied to other fields of image detection, and has high application value.

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  • Engineering & Computer Science (AREA)
  • Multimedia (AREA)
  • Signal Processing (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé permettant de détecter un changement d'une scène vidéo. Le procédé comprend les étapes consistant à : convertir des trames d'image d'un fichier vidéo d'un espace RVB en un espace HSV; diviser le fichier vidéo en une pluralité de séquences vidéo, et acquérir par calcul un histogramme de teinte de calcul de moyenne de chaque séquence vidéo; effectuer un traitement de normalisation sur chaque histogramme de teinte de calcul de moyenne; réaliser un calcul de correspondance sur les histogrammes de teinte de calcul de moyenne de chaque groupe de deux séquences vidéo adjacentes pour obtenir un coefficient de correspondance; et si le coefficient de correspondance entre les histogrammes de teinte de calcul de moyenne des deux séquences vidéo adjacentes correspondantes est supérieur à un seuil de correspondance prédéterminé, considérer les deux séquences vidéo adjacentes comme étant des séquences vidéo de différentes scènes; dans le cas contraire, considérer les deux séquences vidéo adjacentes comme étant des séquences vidéo de la même scène. Par conséquent, une détection de scène vidéo rapide est réalisée sur la base de séquences vidéo selon des différences de teinte principales entre des séquences vidéo adjacentes. La présente invention peut en outre être appliquée à d'autres champs de détection d'image, et a une valeur d'application élevée.
PCT/CN2014/092640 2014-09-11 2014-12-01 Procédé de détection d'un changement d'une scène vidéo WO2016037422A1 (fr)

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CN201410461282.5A CN104184925A (zh) 2014-09-11 2014-09-11 一种视频场景变化的检测方法
CN201410461282.5 2014-09-11

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CN110930464A (zh) * 2019-06-27 2020-03-27 北京中科慧眼科技有限公司 一种基于色调直方图统计的颜色检测方法，装置与系统
CN113591564A (zh) * 2021-06-24 2021-11-02 贵州国致科技有限公司 一种场景异常状态检测方法
CN114155254A (zh) * 2021-12-09 2022-03-08 成都智元汇信息技术股份有限公司 基于图像校正的切图方法、电子设备及介质
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CN105005772B (zh) * 2015-07-20 2018-06-12 北京大学 一种视频场景检测方法
CN105912981A (zh) * 2016-03-31 2016-08-31 乐视控股（北京）有限公司 视频转场判断方法及装置
CN106686452B (zh) * 2016-12-29 2020-03-27 北京奇艺世纪科技有限公司 一种动态图片的生成方法及装置
CN108280386B (zh) * 2017-01-05 2020-08-28 浙江宇视科技有限公司 监控场景检测方法及装置
CN107277650B (zh) * 2017-07-25 2020-01-21 中国华戎科技集团有限公司 视频文件切割方法及装置
CN114120197B (zh) * 2021-11-27 2024-03-29 中国传媒大学 2si模式传输的超高清视频异态信号检测方法

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Cited By (8)

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Publication number Priority date Publication date Assignee Title
CN108184078A (zh) * 2017-12-28 2018-06-19 可贝熊（湖北）文化传媒股份有限公司 一种视频处理系统及其方法
CN110930464A (zh) * 2019-06-27 2020-03-27 北京中科慧眼科技有限公司 一种基于色调直方图统计的颜色检测方法，装置与系统
CN113591564A (zh) * 2021-06-24 2021-11-02 贵州国致科技有限公司 一种场景异常状态检测方法
CN113591564B (zh) * 2021-06-24 2024-05-03 贵州国致科技有限公司 一种场景异常状态检测方法
CN114155254A (zh) * 2021-12-09 2022-03-08 成都智元汇信息技术股份有限公司 基于图像校正的切图方法、电子设备及介质
CN114155254B (zh) * 2021-12-09 2022-11-08 成都智元汇信息技术股份有限公司 基于图像校正的切图方法、电子设备及介质
CN116612110A (zh) * 2023-07-14 2023-08-18 微山县振龙纺织品有限公司 用于渐变印染效果的质量智能评估方法
CN116612110B (zh) * 2023-07-14 2023-10-24 微山县振龙纺织品有限公司 用于渐变印染效果的质量智能评估方法

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