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    Zero-watermarking registration and detection method for HEVC video streaming against requantization transcoding
    11.
    发明授权
    Zero-watermarking registration and detection method for HEVC video streaming against requantization transcoding 有权

    公开(公告)号：US09947065B2

    公开(公告)日：2018-04-17

    申请号：US15045256

    申请日：2016-02-17

    Applicant: Ningbo University

    Inventor： Gangyi Jiang ,  Jing Wang ,  Mei Yu ,  Fen Chen

    IPC: G06K9/00 ,  G06T1/00 ,  H04N19/467 ,  H04N19/40

    CPC classification number: G06T1/0028 ,  G06T1/0057 ,  G06T2201/0053 ,  H04N19/40 ,  H04N19/467

    Abstract: A zero-watermarking registration and detection method for HEVC video streaming against requantization transcoding is provided. To increase an attack-resistance of a registration watermarking, the registration method firstly processes depth values corresponding to respective brightness blocks in a target video streaming with a depth binarization during constructing registration watermarking information through depth features, because the depth binarization well reflects a robustness of the registration watermarking. A first watermarking information matrix including a part of the depth values after the depth binarization is encrypted with a random matrix, so as to increase a safety of the registration watermarking. The registration method directly generates zero-watermarking through the depth features of the video streaming without modifying original carrier information and affecting a watermarking transparency. Meanwhile, the registration method has a strong robustness against attacks, such as the requantization transcoding of quantization parameters within a certain range of variation and common signal processing.

    Objective assessment method for stereoscopic image quality combined with manifold characteristics and binocular characteristics
    12.
    发明申请
    Objective assessment method for stereoscopic image quality combined with manifold characteristics and binocular characteristics 审中-公开
    立体图像质量的客观评估方法与歧管特征和双目特征相结合

    公开(公告)号：US20160350941A1

    公开(公告)日：2016-12-01

    申请号：US15233950

    申请日：2016-08-11

    Applicant: Ningbo University

    Inventor： Mei Yu ,  Zhaoyun Wang ,  Fen Chen ,  Meiling He

    IPC: G06T7/40

    CPC classification number: G06T7/0002 ,  G06K9/4661 ,  G06K9/6252 ,  G06T2207/10012 ,  G06T2207/20081 ,  G06T2207/30168 ,  H04N13/00 ,  H04N13/133

    Abstract: An objective assessment method for a stereoscopic image quality combined with manifold characteristics and binocular characteristics trains a matrix after dimensionality reduction and whitening obtained from natural scene plane images through an orthogonal locality preserving projection algorithm, for obtaining a best mapping matrix. Image blocks, not important for visual perception, are removed. After finishing selecting the image blocks, through the best mapping matrix, manifold characteristic vectors of the image blocks are extracted, and a structural distortion of a distorted image is measured according to a manifold characteristic similarity. Considering influences of an image luminance variation on human eyes, a luminance distortion of the distorted image is calculated according to a mean value of the image blocks. After obtaining the manifold similarity and the luminance similarity, quality values of the left and right viewpoint images are processed with linear weighting to obtain a quality value of the distorted stereoscopic image.

    Abstract translation: 对于与歧管特征和双目特征组合的立体图像质量的客观评估方法，通过正交局部保留投影算法从自然场景平面图像获得的维数降低和白化之后训练矩阵，以获得最佳映射矩阵。 图像块，对视觉感知不重要，被删除。 在完成选择图像块之后，通过最佳映射矩阵，提取图像块的多重特征向量，并根据流形特征相似度来测量失真图像的结构失真。 考虑到图像亮度变化对人眼的影响，根据图像块的平均值计算失真图像的亮度失真。 在获得歧管相似性和亮度相似性之后，利用线性加权处理左和右视点图像的质量值，以获得失真的立体图像的质量值。

    Video quality objective assessment method based on spatiotemporal domain structure
    16.
    发明授权
    Video quality objective assessment method based on spatiotemporal domain structure 有权

    公开(公告)号：US09756323B2

    公开(公告)日：2017-09-05

    申请号：US15214456

    申请日：2016-07-20

    Applicant: Ningbo University

    Inventor： Mei Yu ,  Yaqi Lv ,  Fen Chen ,  Shanshan Liu

    IPC: H04N17/02 ,  H04N19/154 ,  H04N19/89

    CPC classification number: H04N17/02 ,  H04N19/154 ,  H04N19/89

    Abstract: A video quality objective assessment method based on a spatiotemporal domain structure firstly combines a spatiotemporal domain gradient magnitude and color information for calculating a spatiotemporal domain local similarity, and then uses variance fusion for spatial domain fusion. The spatiotemporal domain local similarity is fused into frame-level objective quality value, and then a temporal domain fusion model is established by simulating three important global temporal effects, which are a smoothing effect, an asymmetric track effects and a recency effect, of a human visual system. Finally, the objective quality values of the distorted video sequence are obtained. By modeling the human visual temporal domain effect, the temporal domain weighting method of the present invention is able to accurately and efficiently evaluate the objective quality of the distorted video.

    Image quality objective evaluation method based on manifold feature similarity
    17.
    发明申请
    Image quality objective evaluation method based on manifold feature similarity 审中-公开

    公开(公告)号：US20170177975A1

    公开(公告)日：2017-06-22

    申请号：US15062112

    申请日：2016-03-06

    Applicant: Ningbo University

    Inventor： Mei Yu ,  Zhaoyun Wang ,  Zongju Peng ,  Fen Chen ,  Yang Song

    IPC: G06K9/62 ,  G06K9/46 ,  G06K9/52 ,  G06T7/40

    CPC classification number: G06K9/6256 ,  G06K9/036 ,  G06K9/4652 ,  G06K9/6248 ,  G06T7/90 ,  G06T2207/30168

    Abstract: An image quality objective evaluation method based on manifold feature similarity is disclosed, which firstly adopts visual salience and visual threshold to remove image blocks which are unimportant to visual perception, namely, uses roughing selection and fine selection; and then utilizes the best mapping matrix after block selection to extract manifold feature vectors of image blocks which are selected from original undistorted natural scene images and distorted images to be evaluated; and then measures the structural distortion of distorted images according to manifold feature similarity; and then considers effects of image brightness changes on human eyes and obtains the brightness distortion of distorted images based on an average value of image blocks, and finally obtains quality scores according to structural distortion and brightness distortion; which allows the method of the present invention to have a higher evaluation accuracy, and also expands the evaluation capacity to various distortions.

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