WO2017101347A1 - 动画视频识别与编码方法及装置 - Google Patents

动画视频识别与编码方法及装置 Download PDF

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WO2017101347A1
WO2017101347A1 PCT/CN2016/088689 CN2016088689W WO2017101347A1 WO 2017101347 A1 WO2017101347 A1 WO 2017101347A1 CN 2016088689 W CN2016088689 W CN 2016088689W WO 2017101347 A1 WO2017101347 A1 WO 2017101347A1
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video
parameter
identified
model
parameters
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French (fr)
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刘阳
蔡砚刚
魏伟
白茂生
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Le Holdings Beijing Co Ltd
LeCloud Computing Co Ltd
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Le Holdings Beijing Co Ltd
LeCloud Computing Co Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/50—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding
    • H04N19/503—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using predictive coding involving temporal prediction
    • H04N19/51—Motion estimation or motion compensation
    • H04N19/56—Motion estimation with initialisation of the vector search, e.g. estimating a good candidate to initiate a search
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/134—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or criterion affecting or controlling the adaptive coding
    • H04N19/136—Incoming video signal characteristics or properties
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/115—Selection of the code volume for a coding unit prior to coding
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/102—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the element, parameter or selection affected or controlled by the adaptive coding
    • H04N19/124—Quantisation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/17—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object
    • H04N19/172—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being an image region, e.g. an object the region being a picture, frame or field
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/177—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a group of pictures [GOP]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N19/00—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals
    • H04N19/10—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding
    • H04N19/169—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding
    • H04N19/186—Methods or arrangements for coding, decoding, compressing or decompressing digital video signals using adaptive coding characterised by the coding unit, i.e. the structural portion or semantic portion of the video signal being the object or the subject of the adaptive coding the unit being a colour or a chrominance component
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs
    • H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream

Definitions

  • the present invention relates to the field of video technologies, and in particular, to an animation video recognition and encoding method and apparatus.
  • the video needs to be re-encoded so that the user can view it smoothly and clearly.
  • animation video content is simple, characterized by concentrated color distribution and sparse line outlines.
  • the encoding parameters required for the animated video may be different from the encoding parameters required for the video of the conventional content. For example, for animated video, the code rate of the code can be reduced, but the definition of the video of the conventional content can be obtained at a high bit rate.
  • the embodiment of the invention provides an animation video recognition and encoding method and device, which are used to solve the defect that the user needs to manually switch the video output mode in the prior art, and realize automatic switching of the video output mode.
  • the invention provides an animation video recognition and coding method, comprising:
  • the encoding parameters and the code rate of the to-be-identified video are adjusted.
  • the invention also provides an animation video recognition and encoding device, comprising:
  • a parameter obtaining module configured to perform a dimensionality reduction process on the to-be-identified video, and obtain an input feature parameter of the to-be-identified video
  • a judging module configured to call a pre-trained feature model according to the input feature parameter, and determine whether the to-be-recognized video is an animated video
  • the encoding module is configured to adjust an encoding parameter of the to-be-identified video and a code rate when determining that the to-be-identified video is an animated video.
  • the present invention also provides an animation video recognition and encoding device, including: a memory, a processor, wherein
  • the memory is configured to store one or more instructions, wherein the one or more instructions are for execution by the processor;
  • the processor is configured to perform a dimension reduction process on the video to be identified, and acquire an input feature parameter of the to-be-identified video;
  • the to-be-identified video is an animated video
  • it is used to adjust an encoding parameter and a code rate of the to-be-identified video.
  • the present invention can obtain the following technical effects:
  • the animation video recognition and encoding method and device provided by the invention automatically recognizes the animation video in the video library through the pre-trained feature model, and adjusts the coding parameters while ensuring the consistency with other content videos, thereby Save bandwidth and improve coding efficiency while getting clear video.
  • Embodiment 1 is a technical flowchart of Embodiment 1 of the present invention.
  • Embodiment 2 is a technical flowchart of Embodiment 2 of the present invention.
  • FIG. 3 is a schematic structural diagram of a device according to Embodiment 3 of the present invention.
  • FIG. 4 is a schematic diagram of device connection according to Embodiment 4 of the present invention.
  • an animation video recognition and encoding method mainly includes the following three steps:
  • Step 110 Perform a dimensionality reduction process on the to-be-identified video to obtain an input feature parameter of the to-be-identified video.
  • the dimension-removing process is performed on the to-be-identified video, and the purpose is to extract the input feature parameter of the video frame, and convert the larger dimension of the video frame into a smaller one represented by the feature parameter.
  • the dimensions are matched to the pre-trained feature model to classify the to-be-identified video.
  • the specific dimension reduction process is specifically implemented by the following steps 111 to 113:
  • Step 111 Acquire each video frame of the to-be-processed video, and convert the video frame of the non-RGB color space into an RGB color space.
  • any color light in nature can be added and mixed in different proportions of R, G, and B:
  • Adjusting any of the three color coefficients r, g, b will change the coordinate value of F, that is, change the color value of F.
  • the three primary color components are all 0 (the weakest), they are mixed into black light; when the three primary color components are all k (the strongest), they are mixed into white light.
  • the RGB color space is represented by the physical three primary colors, so the physical meaning is clear. However, this system does not adapt to the visual characteristics of human beings. Thus, other different color space representations have been created, such as CMY color space, CMYK color space, HSI color space, HSV color space, and the like.
  • CMY Cyan, Magenta, and Yellow
  • the range of values of C, M, and Y is [0, 1].
  • CMYK Cyan C, Magenta M, Yellow Y, and Black K
  • the HSI (Hue, Saturation and Intensity) color space is derived from the human visual system, and the color is described by Hue, Saturation or Chroma, and Intensity or Brightness.
  • the HSI color space can be described by a conical space model. When the HSI color space is converted to the RGB color space, the following conversion formula can be taken:
  • Step 112 After converting one frame of image into the RGB color space, the R, G, and B gray histograms corresponding to the RGB color space are counted, and the standard deviations corresponding to the R, G, and B gray histograms are respectively calculated;
  • the R, G, and B gray histograms are hist_R[256], hist_G[256], and hist_B[256].
  • the standard deviations of the hist_R[256], hist_G[256], and hist_B[256] are calculated as sd_R, sd_G, and sd_B, respectively.
  • Step 113 Perform edge detection processing on the video frame in the R, G, and B color channels, respectively, to obtain the number of contours of the R, G, and B color channels in the video frame.
  • Edge detection processing is performed on each of the R, G, and B channel images, and then the number of contours in each image is counted as c_R, c_G, and c_B, respectively.
  • the input characteristic parameters of the to-be-processed video are obtained, that is, the standard deviations sd_R, sd_G, sd_B and the number of contours c_R, c_G, c_B corresponding to the R, G, and B color channels, respectively.
  • Step 120 Call a pre-trained feature model according to the input feature parameter, and determine whether the to-be-recognized video is an animated video;
  • the pre-trained feature model is as follows:
  • x is an input characteristic parameter of the video to be identified
  • x i is an input characteristic parameter of the video sample
  • f(x) is a classification of the video to be identified
  • sgn() is a symbol function characteristic
  • a nuclear function is the relevant parameters of the feature model.
  • the return value of the symbolic function is only two, 1 or -1, and the symbolic function can be represented more vividly by the step signal u(x):
  • one or -1 that is, two possibilities of the video to be processed can be obtained by calculation: animated video and non-animated video.
  • the training process of the feature model will be elaborated in the second embodiment below.
  • Step 130 When it is determined that the to-be-identified video is an animated video, adjust an encoding parameter and a code rate of the to-be-identified video.
  • the animation video content is simple, the color distribution is concentrated, and the line outline is sparse, when encoding, the corresponding coding parameters, such as the code rate and the quantization parameter, can be modified, thereby reducing the code rate of the encoding and improving the encoding speed.
  • the video to be processed is subjected to dimensionality reduction processing, and the pre-trained feature model is called to identify whether the video to be processed is an animated video, thereby adjusting the encoding parameters according to the recognition result, and realizing that the video resolution is unchanged. It has high coding efficiency while saving coding bandwidth.
  • Embodiment 2 is a technical flowchart of Embodiment 2 of the present invention. The following part will be specifically described in conjunction with FIG. 2 to specifically describe a training process of a feature model in an animation video recognition and encoding method according to an embodiment of the present invention.
  • a certain number of animated video samples and non-animated video samples are used for training the feature model in advance, and the more the number, the more accurate the model classification is.
  • the video samples are first classified to obtain a positive sample (animated video) and a negative sample (non-animated video).
  • the duration of the video samples is random and random.
  • Step 210 Acquire each video frame of the video sample, and convert the video frame of the non-RGB color space into the RGB color space.
  • n a certain number of necessary features are extracted, and the necessary features are used as dimensions to achieve the purpose of dimension reduction, thereby simplifying the process of model training and reducing the amount of calculation while further reducing the amount of calculation.
  • the feature model is optimized.
  • Step 220 performing the dimensionality reduction processing on the video sample to obtain an input feature parameter of the video sample.
  • the input characteristic parameters of the to-be-processed video that is, the standard deviations sd_R, sd_G, sd_B, and the number of contours c_R of the R, G, and B color channels respectively.
  • c_G, c_B The video frame after the dimensionality reduction process will be reduced from n dimensions to 6 dimensions.
  • Step 230 Train the feature model by using a Support Vector Machine (SVM) according to the input feature parameter of the video sample.
  • SVM Support Vector Machine
  • the SVM type used in the embodiment of the present invention is a nonlinear soft interval classifier C-SVC, as shown in Equation 1:
  • C represents a penalty parameter
  • ⁇ i represents a slack variable corresponding to the i-th sample video
  • x i represents the input feature parameter corresponding to the i-th sample video, that is, a standard corresponding to the R, G, and B color channels respectively.
  • the deviations sd_R, sd_G, sd_B, and the number of contours c_R, c_G, c_B, y i represent the type of the i-th sample video (ie, whether the sample video is an animated video or a non-animated video, for example, 1 can be set to represent an animated video, and -1 to represent a non-animated video.
  • l represents the total number of sample videos, the symbol "
  • Equation 2 The calculation of the parameter w is as shown in Equation 2.
  • x i represents the input feature parameter corresponding to the i-th sample video
  • y i represents the type of the i-th sample video
  • Equation 3 The dual problem of Equation 1 is shown in Equation 3.
  • Equation 4 xi represents the sample feature parameter corresponding to the i-th sample video, x j represents the sample feature parameter corresponding to the j-th sample video, and ⁇ is a tunable parameter of the kernel function.
  • the initial value of the parameter ⁇ of the RBF kernel function is set to 1e-5.
  • Equation 6 the value of j is obtained by selecting a positive component 0 ⁇ ⁇ j * ⁇ C from ⁇ * .
  • Equation 7 the feature model for video recognition as shown in Equation 7 can be obtained:
  • a cross validation algorithm is selected for finding the optimal values of the parameters ⁇ and C for the feature model. Specifically, k-folder cross-validation is employed.
  • K-fold cross-validation the initial sampling is divided into K sub-samples, a single sub-sample is retained as the data of the verification model, and the other K-1 samples are used for training.
  • Cross-validation is repeated K times, each sub-sample is verified once, and the average K-time results or other combinations are used to finally obtain a single estimate.
  • the advantage of this method is that it is repeated at random. The subsamples are trained and verified, and the results are verified once.
  • the folding number k can be selected as 5, the range of the penalty parameter C is set to [0.01, 200], and the range of the parameter ⁇ of the kernel function is set to [1e-6, 4].
  • the steps of ⁇ and C are both selected as 2.
  • the difference between the animated video and the non-animated video is obtained by analyzing the animated video sample and the non-animated video sample, and at the same time, the video is dimension-reduced and characterized by two types of video samples.
  • the parameters are extracted, and the model parameters are trained by using these feature parameters, and the feature model capable of identifying the video to be classified is obtained, so that the coding parameters can be adjusted according to the type of the video, and the bandwidth is saved under the premise of obtaining a clear video.
  • an animation video recognition and encoding apparatus mainly includes the following modules: a parameter acquisition module 310, a determination module 320, an encoding module 330, and a model. Training module 340.
  • the parameter obtaining module 310 is configured to perform a dimensionality reduction process on the to-be-identified video, and acquire an input feature parameter of the to-be-identified video;
  • the determining module 320 is configured to call a pre-trained feature model according to the input feature parameter, and determine whether the to-be-recognized video is an animated video;
  • the encoding module 330 is configured to adjust an encoding parameter of the to-be-identified video and a code rate when determining that the to-be-identified video is an animated video.
  • the parameter obtaining module 310 is further configured to: acquire each video frame of the to-be-processed video, and convert the video frame of the non-RGB color space into an RGB color space; and count the R, G, and B gray levels corresponding to the RGB color space. Histogram, respectively calculating the R, G, B gray scale The standard deviation corresponding to the square map; performing edge detection processing on the video frames in the R, G, and B color channels, respectively, to obtain the number of contours of the R, G, and B color channels in the video frame.
  • the model training module 340 is configured to: invoke the parameter obtaining module to perform the dimensionality reduction processing on the video sample to obtain an input feature parameter of the video sample; wherein the input feature parameter includes the R, G, and B grays The standard deviation corresponding to the degree histogram, the number of contours belonging to the R, G, and B color channels respectively; and the feature model is trained by using the support vector machine model according to the input characteristic parameters of the video sample.
  • model training module 340 trains the feature model as follows:
  • x is an input feature parameter of the video to be identified
  • x i is an input feature parameter of the video sample
  • f(x) is a classification of the video to be identified, according to a symbol function sgn() characteristic, f(x)
  • the output value is 1 or -1, which represents animated video and non-animated video respectively
  • K is a kernel function, and is calculated according to preset adjustable parameters, combined with input characteristic parameters of the video sample
  • b * are the relevant parameters of the feature model
  • b * are calculated according to preset penalty parameters in combination with input feature parameters of the video samples.
  • the model training module 340 is further configured to: when training the feature model by using a support vector machine model, select a cross-validation algorithm to find the adjustable parameter and the penalty parameter, thereby improving generalization capability of the feature model.
  • FIG. 3 performs the embodiment shown in FIG. 1 to FIG. 2 .
  • the implementation principle and technical effects refer to the embodiments shown in FIG. 1 to FIG. 3 , and details are not described herein again.
  • An animation video recognition and encoding device comprising: a memory 401, a processor 402, wherein
  • the memory 401 is configured to store one or more instructions, where the one or more instructions are used by the processor 402 to invoke execution;
  • the processor 402 is configured to perform a dimensionality reduction process on the to-be-identified video to obtain an input feature parameter of the to-be-identified video.
  • the to-be-identified video is an animated video
  • it is used to adjust an encoding parameter and a code rate of the to-be-identified video.
  • the processor 402 is further configured to: acquire each video frame of the to-be-processed video, and convert the video frame of the non-RGB color space into an RGB color space; and calculate the R, G, and B gray scales corresponding to the RGB color space. a figure, respectively calculating a standard deviation corresponding to the R, G, and B gray histograms; performing edge detection processing on the video frames in the R, G, and B color channels, respectively, to obtain that the video frames respectively belong to R, G , the number of contours of the B color channel.
  • the processor 402 is further configured to: perform the dimensionality reduction processing on the video sample to obtain an input feature parameter of the video sample; wherein the input feature parameter includes a standard corresponding to the R, G, and B gray histograms Deviation, the number of contours respectively belonging to the R, G, B color channels; and the support vector model is used to train the feature model according to the input characteristic parameters of the video sample.
  • the processor 402 is further configured to: train the feature model as follows:
  • x is an input feature parameter of the video to be identified
  • x i is an input feature parameter of the video sample
  • f(x) is a classification of the video to be identified, according to a symbol function sgn() characteristic, f(x)
  • the output value is 1 or -1, which represents animated video and non-animated video respectively
  • K is a kernel function, and is calculated according to preset adjustable parameters, combined with input characteristic parameters of the video sample
  • b * are the relevant parameters of the feature model
  • b * are calculated according to preset penalty parameters in combination with input feature parameters of the video samples.
  • the processor 402 is further configured to: when training the feature model by using a support vector machine model, select a cross-validation algorithm to find the adjustable parameter and the penalty parameter, thereby improving generalization capability of the feature model.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment. Those of ordinary skill in the art can understand and implement without deliberate labor.

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Abstract

本发明公开一种动画视频识别与编码方法及装置。将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；当判定所述待识别视频为动画视频，则调整所述待识别视频的编码参数以及码率。在获得清晰视频的前提下，节省带宽、提高编码效率。

Description

动画视频识别与编码方法及装置
交叉引用
本申请引用于2015年12月18日递交的名称为“动画视频识别与编码方法及装置”的第201510958701.0号中国专利申请，其通过引用被全部并入本申请。
技术领域
本发明涉及视频技术领域，尤其涉及一种动画视频识别与编码方法及装置。
背景技术
随着多媒体技术的快速发展，大量的动画类视频被制作并在互联网上传播。
对于视频网站而言，需要将视频进行重新编码以便用户能够流畅、清晰地观看。相对于传统视频内容而言(电视剧、电影等)，动画类视频内容简单，表现为颜色分布集中，线条轮廓稀疏等特点。基于上述特点，在获得相同清晰度的情况下，动画类视频需要的编码参数与传统内容的视频需要的编码参数可以不同。例如对于动画类视频，可以降低编码的码率，但却可以获得与传统内容的视频在高码率情况下一致的清晰度。
因此，一种动画视频识别与编码方法及装置亟待提出。
发明内容
本发明实施例提供一种动画视频识别与编码方法及装置，用以解决现有技术中用户需要手动按键切换视频输出模式的缺陷，实现视频输出模式的自动切换。
本发明提供一种动画视频识别与编码方法，包括：
将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
当判定所述待识别视频为动画视频，则调整所述待识别视频的编码参数以及码率。
本发明还提供一种动画视频识别与编码装置，包括：
参数获取模块，用于将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
判断模块，用于根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
编码模块，当判定所述待识别视频为动画视频，用于调整所述待识别视频的编码参数以及码率。
本发明还提供一种动画视频识别与编码设备，包括：存储器、处理器，其中，
所述存储器，用于存储一条或多条指令，其中，所述一条或多条指令以供所述处理器调用执行；
所述处理器，用于将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
用于根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
当判定所述待识别视频为动画视频，用于调整所述待识别视频的编码参数以及码率。
与现有技术相比，本发明可以获得包括以下技术效果：
本发明提供的动画视频识别与编码方法及装置，通过预先训练的特征模型自动识别出视频库内的动画类视频，并在保证和其他内容视频一致的清晰度的情况下，调整编码参数，从而在获得清晰视频的前提下，节省带宽、提高编码效率。
附图说明
此处所说明的附图用来提供对本发明的进一步理解，构成本申请的一部分，本发明的示意性实施例及其说明用于解释本发明，并不构成对本发明的不当限定。在附图中：
图1为本发明实施例一的技术流程图；
图2为本发明实施例二的技术流程图；
图3为本发明实施例三的装置结构示意图；
图4是本发明实施例四的设备连接示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚，下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本发明一部分实施例，而不是全部的 实施例。基于本发明中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本发明保护的范围。
实施例一
图1是本发明实施例一的技术流程图，参考图1，本发明实施例一种动画视频识别与编码方法，主要包括如下的三个步骤：
步骤110：将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
本发明实施例中，对所述待识别视频进行降维处理，其目的在于，提取视频帧的所述输入特征参数，将视频帧较大的维数转化为用所述特征参数表示的较小的维度，从而与预先训练的特征模型进行匹配，从而对所述待识别视频进行分类。具体降维的过程具体由以下步骤111～步骤113实现：
步骤111：获取所述待处理视频的每一视频帧，并将非RGB颜色空间的视频帧转化至RGB颜色空间。
大量待处理视频的格式不同，其对应的色彩空间也可能是多样的，需要将其转化为同一色彩空间，按照同样的标准和参数对所述待处理视频进行分类，简化了分类计算的复杂度，同时提升了分类的准确性。以下部分将例举非RGB颜色空间转换至RGB颜色空间的转换公式，当然，应当理解，以下部分仅供举例从而对本发明实施例做进一步阐述，但对本发明实施例并不构成限制。任何能实现本发明实施例非RGB颜色空间转换至RGB颜色空间的算法均在本发明实施例的保护范围之内。
如下公式所示，自然界中任何一种色光都可由R、G、B三基色按不同的比例相加混合而成：
F＝r*R+g*G+b*B
调整三色系数r、g、b中的任一系数都会改变F的坐标值，也即改变了F的色值。当三基色分量都为0(最弱)时混合为黑色光；当三基色分量都为k(最强)时混合为白色光。
RGB颜色空间采用物理三基色表示，因而物理意义很清楚。然而这一体制并不适应人的视觉特点。因而，产生了其他不同的颜色空间表示法，例如CMY颜色空间、CMYK颜色空间、HSI颜色空间、HSV颜色空间等。
彩色印刷或彩色打印的纸张是不能发射光线的，因而印刷机或彩色打印机就只能使用一些能够吸收特定的光波而反射其他光波的油墨或颜料。油墨或颜料的3基色是青(Cyan)、品红(Magenta)和黄(Yellow)，简称为CMY。CMY空间正好与RGB空间互补，也即用白色减去RGB空间中的某一颜色值就等于同样颜色在CMY空间中的值，即当CMY颜色空间转化至RGB颜色空间时，可采取如下的转换公式：
Figure PCTCN2016088689-appb-000001
其中，C、M、Y的取值范围是[0,1]。
当CMYK(青C、品红M、黄Y及黑K)颜色空间转化至RGB颜色空间时，可采取如下的转换公式：
R＝1-min{1，C×(1-B)+B}
G＝1-min{1，M×(1-B)+B}
B＝1-min{1，Y×(1-B)+B}
HSI(Hue，Saturation and Intensity)颜色空间是从人的视觉系统出发，用色调(Hue)、色饱和度(Saturation或Chroma)和亮度(Intensity或Brightness)来描述颜色。HSI颜色空间可以用一个圆锥空间模型来描述。当HSI颜色空间转化至RGB颜色空间时，可采取如下的转换公式：
(1)0＜H＜120时，
B＝I(1-S)
Figure PCTCN2016088689-appb-000002
G＝3I-(R+B)
(2)0＜H＜240时，H＝H-120
R＝I(1-s)
Figure PCTCN2016088689-appb-000003
B＝3I-(R+G)
(23)240＜H＜360时，H＝H-240
G＝I(1-S)
Figure PCTCN2016088689-appb-000004
R＝3I-(B+G)
步骤112：将一帧图像转化至RGB颜色空间后，统计RGB颜色空间对应的R、G、B灰度直方图，分别计算所述R、G、B灰度直方图对应的标准偏差；
本步骤中，记所述R、G、B灰度直方图为hist_R[256]、hist_G[256]及hist_B[256]。计算hist_R[256]、hist_G[256]及hist_B[256]的标准偏差分别记为sd_R、sd_G、sd_B。
步骤113：分别在R、G、B颜色通道对所述视频帧进行边缘检测处理，得到所述视频帧内分别属于R、G、B颜色通道的轮廓数量。
对R、G、B各通道图像进行边缘检测处理，之后统计各图像内的轮廓个数，分别记为c_R、c_G、c_B。
由此，便得到了所述待处理视频的所述输入特征参数，即R、G、B颜色通道分别对应的标准偏差sd_R、sd_G、sd_B以及轮廓数量c_R、c_G、c_B。
步骤120：根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
本发明实施例中，预先训练的特征模型如下所示：
Figure PCTCN2016088689-appb-000005
其中，其中，x为所述待识别视频的输入特征参数，xi为所述视频样本的输入特征参数，f(x)为所述待识别视频的分类，sgn()为符号函数特性；K为核函数；
Figure PCTCN2016088689-appb-000006
和b*为所述特征模型的相关参数。
符号函数的返回值只有两个，1或-1，可以用阶跃信号u(x)更加形象地表示符号函数：
Figure PCTCN2016088689-appb-000007
因此，将步骤110中获取的所述输入特征参数输入特征模型，便可通过计算得到1或-1，即待处理视频的两种可能：动画视频和非动画视频。特征模型的训练过程将在下述实施例二中详细阐述。
步骤130：当判定所述待识别视频为动画视频，则调整所述待识别视频的编码参数以及码率。
由于动画类视频内容简单，颜色分布集中，线条轮廓稀疏，因此编码时，可以修改相应的编码参数，例如码率、量化参数等，从而降低编码的码率，提高编码速度。
本实施例通过将待处理的视频进行降维处理，并调用预先训练的特征模型识别待处理的视频是否为动画视频，从而根据识别结果调整编码参数，实现了视频清晰度不变的情况下，具有较高的编码效率，同时节省了编码带宽。
实施例二
图2是本发明实施例二的技术流程图，以下部分将结合图2，具体阐述本发明实施例一种动画视频识别与编码方法中，特征模型的训练过程。
本发明实施例中，预先采用一定数量的动画视频样本和非动画视频样本进行特征模型的训练，数量越多，则训练出的模型分类越准确。首先将视频样本进行分类，得到正样本(动画视频)和负样本(非动画视频)。视频样本的时长随机、内容随机。
步骤210：获取视频样本的每一视频帧，并将非RGB颜色空间的视频帧转化至RGB颜色空间；
分析正负样本特征发现，正样本与负样本的明显区别是，正样本帧内颜色分布集中，线条轮廓稀疏。因此，本发明以上述特征作为训练输入特征。对于样本的每一帧，当其采用YUV420格式时，输入空间的维数为n＝width*height*2，其中width和height分别表示视频帧的宽度和高度，这样的数据量处理起来比较困难，因此本发明实施例首先对于视频样本进行降维处理。具体地，对维数为n的每一视频帧，提取其一定数量的必要特征，并以所述必要特征作为维度，以实现降维目的，从而简化模型训练的过程，减少计算量的同时进一步优化了特征模型。
具体降维处理的执行原理及技术效果同步骤110所述，不再赘述。
步骤220：对视频样本进行所述降维处理从而获取所述视频样本的输入特征参数；
同实施例一中所述，所述待处理视频的所述输入特征参数，即R、G、B颜色通道分别对应的标准偏差sd_R、sd_G、sd_B以及轮廓数量c_R、 c_G、c_B。降维处理后的所述视频帧将由n个维度降至6个维度。
步骤230：根据所述视频样本的输入特征参数，采用支持向量机模型(Support Vector Machine，SVM)训练所述特征模型。
具体地，本发明实施例使用的SVM类型是非线性软间隔分类机C-SVC，如公式1所示：
Figure PCTCN2016088689-appb-000008
subject to:
yi((w×xi+b))≥1-εi,i＝1,...,l
εi≥0,i＝1,...,l
C>0              公式1
公式1中，C表示惩罚参数，εi表示第i个样本视频对应的松弛变量，xi表示第i个样本视频对应的所述输入特征参数，即R、G、B颜色通道分别对应的标准偏差sd_R、sd_G、sd_B以及轮廓数量c_R、c_G、c_B，yi表示第i个样本视频的类型(即样本视频是动画视频还是非动画视频，例如可以设置1表示动画视频，-1表示非动画视频等)；l表示样本视频的总个数，符号“|| ||”表示范数，w和b是相关参数；“subject to”表示“约束于”的，其使用形式如公式1，即目标函数subject to约束条件。
参数w的计算如公式2所示，
Figure PCTCN2016088689-appb-000009
公式2中，xi表示第i个样本视频对应的所述输入特征参数，yi表示第i个样本视频的类型。
公式1的对偶问题如公式3所示，
Figure PCTCN2016088689-appb-000010
s.t.:
Figure PCTCN2016088689-appb-000011
0≤αi≤C,i＝1,...,l          公式3
公式3中，s.t.＝subject to，表示位于s.t前的目标函数约束于位于s.t后的约束条件；xi表示第i个样本视频对应的所述输入特征参数，yi表示第i个样本视频的类型；xj表示第j个样本视频对应的所述输入特征参数，yj表示第j个样本视频的类型；a是公式1和公式2求得的最优解；C表示惩罚参数，本实施例中，所述惩罚参数C的初始值设置为0.1；l表示样本视频的总个数；K(xi,xj)表示核函数，本发明实施例中的核函数选用RBF核函数(Radial Basis Function，径向基核函数)，核函数如公式4所示：
Figure PCTCN2016088689-appb-000012
公式4中，xi表示第i个样本视频对应的样本特征参数，xj表示第j个样本视频对应的样本特征参数，σ为核函数的可调参数。本实施例中，将RBF核函数的参数σ的初始值设置为1e-5。
根据上述公式1-公式4可以计算得出公式3的最优解，如公式5所示：
α*＝(α1 *,...,αl *)T       公式5
根据α*可以计算得到b*，如公式6所示：
Figure PCTCN2016088689-appb-000013
公式6中，通过从α*中选取一个正分量0<αj *<C得到j的数值。
其次，根据上述相关参数α*和b*即可得到如公式7所示的用于视频识别的特征模型：
Figure PCTCN2016088689-appb-000014
此外，需要说明的是，本发明实施例中，为了提高训练模型的泛化能力，针对所述特征模型，选用交叉验证(Cross validation)算法寻找参数σ与C的最优值。具体地，采用K折交叉验证(k-folder cross-validation)。
K折交叉验证，初始采样分割成K个子样本，一个单独的子样本被保留作为验证模型的数据，其他K-1个样本用来训练。交叉验证重复K次，每个子样本验证一次，平均K次的结果或者使用其它结合方式，最终得到一个单一估测。这个方法的优势在于，同时重复运用随机产生的 子样本进行训练和验证，每次的结果验证一次。
本发明实施例中，可以选取折数k为5，惩罚参数C的范围设置为[0.01，200]，核函数的参数σ的范围设置为[1e-6，4]。验证过程中σ与C的步长均选择2。
本实施例中，通过对动画视频样本和非动画视频样本进行分析，得到动画视频和非动画视频的区别之处，与此同时，对视频进行降维并通过对两种类型的视频样本进行特征参数的提取，并利用这些特征参数进行模型的训练，得到了能够识别待分类视频的特征模型，从而能够根据视频的类型进行编码参数的调整，在获得清晰的视频前提下，带来节省带宽、提高编码速度等有益效果。
实施例三
图3是本发明实施例三的装置结构示意图，结合图3，本发明实施例一种动画视频识别与编码装置，主要包括如下的模块：参数获取模块310、判断模块320、编码模块330、模型训练模块340。
所述参数获取模块310，用于将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
所述判断模块320，用于根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
所述编码模块330，当判定所述待识别视频为动画视频，用于调整所述待识别视频的编码参数以及码率。
所述参数获取模块310进一步用于：获取所述待处理视频的每一视频帧，并将非RGB颜色空间的视频帧转化至RGB颜色空间；统计RGB颜色空间对应的R、G、B灰度直方图，分别计算所述R、G、B灰度直 方图对应的标准偏差；分别在R、G、B颜色通道对所述视频帧进行边缘检测处理，得到所述视频帧内分别属于R、G、B颜色通道的轮廓数量。
所述模型训练模块340用于：调用所述参数获取模块对视频样本进行所述降维处理从而获取所述视频样本的输入特征参数；其中所述输入特征参数包括所述R、G、B灰度直方图对应的标准偏差、所述分别属于R、G、B颜色通道的轮廓数量；根据所述视频样本的输入特征参数，采用支持向量机模型训练所述特征模型。
具体地，所述模型训练模块340训练如下所述特征模型：
Figure PCTCN2016088689-appb-000015
其中，x为所述待识别视频的输入特征参数，xi为所述视频样本的输入特征参数，f(x)为所述待识别视频的分类，根据符号函数sgn()特性，f(x)的输出值为1或-1，分别表示动画视频与非动画视频；K为核函数，根据预设的可调参数，结合所述视频样本的输入特征参数进行计算；
Figure PCTCN2016088689-appb-000016
和b*为所述特征模型的相关参数，
Figure PCTCN2016088689-appb-000017
和b*根据预设的惩罚参数，结合所述视频样本的输入特征参数进行计算。
所述模型训练模块340进一步还用于：采用支持向量机模型训练所述特征模型时，选用交叉验证算法寻找所述可调参数以及所述惩罚参数，从而提高所述特征模型的泛化能力。
图3对应装置执行图1～图2所示实施例，实现原理和技术效果参考图1～图3所示实施例，不再赘述。
实施例四
图4是本发明实施例四的装置结构示意图，结合图4，本发明实施 例一种动画视频识别与编码设备，包括：存储器401、处理器402，其中，
所述存储器401，用于存储一条或多条指令，其中，所述一条或多条指令以供所述处理器402调用执行；
所述处理器402，用于将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
用于根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
当判定所述待识别视频为动画视频，用于调整所述待识别视频的编码参数以及码率。
所述处理器402进一步用于：获取所述待处理视频的每一视频帧，并将非RGB颜色空间的视频帧转化至RGB颜色空间；统计RGB颜色空间对应的R、G、B灰度直方图，分别计算所述R、G、B灰度直方图对应的标准偏差；分别在R、G、B颜色通道对所述视频帧进行边缘检测处理，得到所述视频帧内分别属于R、G、B颜色通道的轮廓数量。
所述处理器402进一步用于：对视频样本进行所述降维处理从而获取所述视频样本的输入特征参数；其中所述输入特征参数包括所述R、G、B灰度直方图对应的标准偏差、所述分别属于R、G、B颜色通道的轮廓数量；根据所述视频样本的输入特征参数，采用支持向量机模型训练所述特征模型。
具体地，所述处理器402进一步用于：训练如下所述特征模型：
Figure PCTCN2016088689-appb-000018
其中，x为所述待识别视频的输入特征参数，xi为所述视频样本的输 入特征参数，f(x)为所述待识别视频的分类，根据符号函数sgn()特性，f(x)的输出值为1或-1，分别表示动画视频与非动画视频；K为核函数，根据预设的可调参数，结合所述视频样本的输入特征参数进行计算；
Figure PCTCN2016088689-appb-000019
和b*为所述特征模型的相关参数，
Figure PCTCN2016088689-appb-000020
和b*根据预设的惩罚参数，结合所述视频样本的输入特征参数进行计算。
所述处理器402进一步还用于：采用支持向量机模型训练所述特征模型时，选用交叉验证算法寻找所述可调参数以及所述惩罚参数，从而提高所述特征模型的泛化能力。
本设备的技术方案和各模块的功能特征、连接方式，与图1～图3对应实施例所描述的特征和技术方案相对应，不足之处请参见前述图1～图3对应实施例。
以上所描述的装置实施例仅仅是示意性的，其中所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。本领域普通技术人员在不付出创造性的劳动的情况下，即可以理解并实施。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到各实施方式可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件。基于这样的理解，上述技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出来，该计算机软件产品可以存储在计算机可读存储介质中，如ROM/RAM、磁碟、光盘等，包括若干指令用以使得一台计算机装置(可以是个人计算机，服务器，或者网络装置等)执行各个实施例或者实施例的某些部分所述的方法。
最后应说明的是：以上实施例仅用以说明本发明的技术方案，而非对其限制；尽管参照前述实施例对本发明进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (10)

  1. 一种动画视频识别与编码方法，其特征在于，包括如下的步骤：
    将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
    根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
    当判定所述待识别视频为动画视频，则调整所述待识别视频的编码参数以及码率。
  2. 根据权利要求1所述的方法，其特征在于，将待识别视频进行降维处理，进一步包括：
    获取所述待处理视频的每一视频帧，并将非RGB颜色空间的视频帧转化至RGB颜色空间；
    统计RGB颜色空间对应的R、G、B灰度直方图，分别计算所述R、G、B灰度直方图对应的标准偏差；
    分别在R、G、B颜色通道对所述视频帧进行边缘检测处理，得到所述视频帧内分别属于R、G、B颜色通道的轮廓数量。
  3. 根据权利要求1或2所述的方法，其特征在于，所述方法进一步包括采用如下步骤预先训练所述特征模型：
    对视频样本进行所述降维处理从而获取所述视频样本的输入特征参数；其中所述输入特征参数包括所述R、G、B灰度直方图对应的标准偏差、所述分别属于R、G、B颜色通道的轮廓数量；
    根据所述视频样本的输入特征参数，采用支持向量机模型训练所述特征模型。
  4. 根据权利要求3所述的方法，其特征在于，采用支持向量机模型 训练所述特征模型，进一步包括：
    所述特征模型以如下公式展示：
    Figure PCTCN2016088689-appb-100001
    其中，x为所述待识别视频的输入特征参数，xi为所述视频样本的输入特征参数，f(x)为所述待识别视频的分类，根据符号函数sgn()特性，f(x)的输出值为1或-1，分别表示动画视频与非动画视频；K为核函数，根据预设的可调参数，结合所述视频样本的输入特征参数进行计算；
    Figure PCTCN2016088689-appb-100002
    和b*为所述特征模型的相关参数，
    Figure PCTCN2016088689-appb-100003
    和b*根据预设的惩罚参数，结合所述视频样本的输入特征参数进行计算。
  5. 根据要求4所述的方法，其特征在于，所述方法还包括：
    采用支持向量机模型训练所述特征模型时，选用交叉验证算法寻找所述可调参数以及所述惩罚参数，从而提高所述特征模型的泛化能力。
  6. 一种动画视频识别与编码装置，其特征在于，包括如下的模块：
    参数获取模块，用于将待识别视频进行降维处理，获取所述待识别视频的输入特征参数；
    判断模块，用于根据所述输入特征参数调用预先训练的特征模型，判断所述待识别视频是否为动画视频；
    编码模块，当判定所述待识别视频为动画视频，用于调整所述待识别视频的编码参数以及码率。
  7. 根据权利要求6所述的装置，其特征在于，所述参数获取模块进一步用于：
    获取所述待处理视频的每一视频帧，并将非RGB颜色空间的视频帧转化至RGB颜色空间；
    统计RGB颜色空间对应的R、G、B灰度直方图，分别计算所述R、G、B灰度直方图对应的标准偏差；
    分别在R、G、B颜色通道对所述视频帧进行边缘检测处理，得到所述视频帧内分别属于R、G、B颜色通道的轮廓数量。
  8. 根据权利要求6或7所述的装置，其特征在于，所述装置进一步包括模型训练模块，所述模型训练模块用于：
    调用所述参数获取模块对视频样本进行所述降维处理从而获取所述视频样本的输入特征参数；其中所述输入特征参数包括所述R、G、B灰度直方图对应的标准偏差、所述分别属于R、G、B颜色通道的轮廓数量；
    根据所述视频样本的输入特征参数，采用支持向量机模型训练所述特征模型。
  9. 根据权利要求8所述的装置，其特征在于，所述模型训练模块进一步用于：
    训练如下所述特征模型：
    Figure PCTCN2016088689-appb-100004
    其中，x为所述待识别视频的输入特征参数，xi为所述视频样本的输入特征参数，f(x)为所述待识别视频的分类，根据符号函数sgn()特性，f(x)的输出值为1或-1，分别表示动画视频与非动画视频；K为核函数，根据预设的可调参数，结合所述视频样本的输入特征参数进行计算；
    Figure PCTCN2016088689-appb-100005
    和b*为所述特征模型的相关参数，
    Figure PCTCN2016088689-appb-100006
    和b*根据预设的惩罚参数，结合所述视频样本的输入特征参数进行计算。
  10. 根据要求9所述的装置，其特征在于，所述模型训练模块进一 步还用于：
    采用支持向量机模型训练所述特征模型时，选用交叉验证算法寻找所述可调参数以及所述惩罚参数，从而提高所述特征模型的泛化能力。
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