Abstract: A method for encoding space-time signals comprises: collecting space-time signals of various local spatial positions in a monitoring area, and accumulating the space-time signals according to time, so as to obtain cumulative signal intensity values; transforming the cumulative signal intensity values by means of a filter, and outputting a pulse signal when a transformation result exceeds a specific threshold; arranging pulse signals corresponding to a local spatial position into a sequence according to the time, so as to obtain a pulse sequence expressing the local spatial position signals and a change process thereof; and arranging the pulse sequences of all local spatial positions into a pulse sequence array according to interrelation among the spatial positions to serve as an encoding for dynamic space-time signals of the monitoring area.

Abstract: Embodiments of the present disclosure provide a method, an apparatus, and a system for deep feature coding and decoding. The method comprises: extracting features of respective video frames; determining types of the features, the types reflecting time-domain correlation degrees between the features and a reference feature; encoding the features using predetermined coding patterns matching the types to obtain coded features; and transmitting the coded features to the server such that the server decodes the coded features for a vision analysis task. By using the embodiments of the present disclosure, videos per se may not be transmitted to the cloud server; instead, the features of the video, after being encoded, are transmitted to the cloud server for a vision analysis task; compared with the prior art, data transmission pressure may be lowered, and the storage pressure at the cloud server may also be lowered.

Abstract: An arithmetic encoding-decoding method for compression of a video image block. The method includes an encoding process and a decoding process. The encoding process includes: 1) acquiring an information of an image block to be encoded; 2) extracting an encoding command of a weighted skip model; 3) acquiring an index of a reference frame according to the information of the image block to be encoded and the command of the weighted skip model, in which the reference frame includes a prediction block for reconstructing the image block to be encoded; 4) acquiring a context-based adaptive probability model for encoding; and 5) performing arithmetic encoding of the index of the reference frame and writing arithmetic codes into an arithmetically encoded bitstream according to the context-based adaptive probability model for encoding.

Abstract: A content server comprising a processor coupled to the receiver configured to obtain a media content component, identify an ownership of the media content component, and generate a Dynamic Adaptive Streaming over Hypertext Transfer Protocol (HTTP) (DASH) Media Presentation Description (MPD) indicating storage locations of segments of the media content component, indicating instructions for displaying the segments of the media content component, and indicating ownership of the media content component, a receiver coupled to the processor and configured to receive a request from a client for the media content component, and a transmitter coupled to the processor and configured to transmit the DASH MPD indicating the ownership of the media content component to the client in response to receiving the request.

Abstract: A method for video encoding based on an image super-resolution, the method including: 1) performing super-resolution interpolation on a video image to be encoded using a pre-trained texture dictionary database to yield a reference image; in which the texture dictionary database includes: one or multiple dictionary bases, and each dictionary basis includes a mapping group formed by a relatively high resolution image block of a training image and a relatively low resolution image block corresponding to the relatively high resolution image block; 2) performing motion estimation and motion compensation of image blocks of the video image on the reference image to acquire prediction blocks corresponding to the image blocks of the video image; 3) performing subtraction between the image blocks of the video image and the corresponding prediction blocks to yield prediction residual blocks, respectively; and 4) encoding the prediction residual blocks.

Abstract: The present invention provides a method and a device for detecting interest points in an image. The method includes: acquiring an original input image; performing down-sampling processing on the original input image, so as to obtain a plurality of sampling images with different resolutions; dividing each sampling image into a plurality of small image blocks; performing filtering processing on the plurality of small image blocks in each sampling image in sequence by using Laplacian-of-Gaussian filters, so as to obtain filtered images of the plurality of small image blocks in each sampling image; and acquiring interest points in an image in filtered images of the plurality of small image blocks in each sampling image. The present invention is used for solving the problems of more memory consumption and a low detection speed in the prior art.

Abstract: A super-resolution image reconstruction apparatus based on a classified dictionary database. The apparatus can select, from a training image, a first local block and a corresponding second down-sampled local block, extract corresponding features and combine the features into a dictionary group, and perform classification and pre-training on multiple dictionary groups by using calculated values of an LBS and an SES as classification marks, so as to obtain a classified dictionary database of multiple dictionary groups with classification marks. During image reconstruction, local features of a local block on an image to be reconstructed are extracted, the LBS and SES classification of the local block is matched with the LBS and SES classification of each dictionary in the classified dictionary database, so that matched dictionaries can be rapidly obtained, and lastly, image reconstruction is performed on the image to be reconstructed by using the matched dictionaries.

Abstract: A method and apparatus for rapidly reconstructing a super-resolution image. In the method and apparatus for rapidly reconstructing a super-resolution image provided in the present application, an original image is processed at least by means of iterative backward mapping based on a texture structural constraint during reconstruction of a super-resolution image of the original image, so as to enhance texture details of the image, thereby improving the high-frequency detail quality of the super-resolution image.

Abstract: A method for compressing a local feature descriptor includes that: at least one local feature descriptor of a target image is selected; and multi-stage vector quantization is carried out on the selected at least one local feature descriptor according to a pre-set code book, and the local feature descriptor is quantized as a feature code stream, wherein the feature code stream includes serial numbers of code words obtained by means of the multi-stage vector quantization. A device for compressing a local feature descriptor and a storage medium are also provided.

Abstract: A media decoding method based on cloud computing and decoder thereof are provided by embodiments of the present invention, which are easy to use and applicable to a media of any form, and its requirement for computer resource is low. The method includes: extracting representing features from a media code stream to be decoded; searching in the cloud for a media object which has similar representing features with the media code stream to be decoded by using a feature matching method and the representing features extracted; filling, replacing and improving parts or segments of the media code stream to be decoded with whole or parts of the media object.

Abstract: A method for video encoding based on a dictionary database, the method including: 1) dividing a current image frame to be encoded in a video stream into a plurality of image blocks; 2) recovering encoding distortion information of a decoded and reconstructed image of a previous frame of the current image frame using a texture dictionary database to obtain an image with recovered encoding distortion information, and performing temporal prediction using the image with the recovered encoding distortion information as a reference image to obtain prediction blocks of image blocks to be encoded; in which, the texture dictionary database includes: clear image dictionaries and distorted image dictionaries corresponding to the clear image dictionaries; and 3) performing subtraction between the image blocks to be encoded and the prediction blocks to obtain residual blocks, and processing the residual blocks to obtain a video bit stream.

Abstract: A method for video encoding based on an image super-resolution, the method including: 1) performing super-resolution interpolation on a video image to be encoded using a pre-trained texture dictionary database to yield a reference image; in which the texture dictionary database includes: one or multiple dictionary bases, and each dictionary basis includes a mapping group formed by a relatively high resolution image block of a training image and a relatively low resolution image block corresponding to the relatively high resolution image block; 2) performing motion estimation and motion compensation of image blocks of the video image on the reference image to acquire prediction blocks corresponding to the image blocks of the video image; 3) performing subtraction between the image blocks of the video image and the corresponding prediction blocks to yield prediction residual blocks, respectively; and 4) encoding the prediction residual blocks.

Abstract: An arithmetic encoding-decoding method for compression of a video image block. The method includes an encoding process and a decoding process. The encoding process includes: 1) acquiring an information of an image block to be encoded; 2) extracting an encoding command of a weighted skip model; 3) acquiring an index of a reference frame according to the information of the image block to be encoded and the command of the weighted skip model, in which the reference frame includes a prediction block for reconstructing the image block to be encoded; 4) acquiring a context-based adaptive probability model for encoding; and 5) performing arithmetic encoding of the index of the reference frame and writing arithmetic codes into an arithmetically encoded bitstream according to the context-based adaptive probability model for encoding.

Abstract: A P frame-based multi-hypothesis motion compensation method includes: taking an encoded image block adjacent to a current image block as a reference image block and obtaining a first motion vector of the current image block by using a motion vector of the reference image block, the first motion vector pointing to a first prediction block; taking the first motion vector as a reference value and performing joint motion estimation on the current image block to obtain a second motion vector of the current image block, the second motion vector pointing to a second prediction block; and performing weighted averaging on the first prediction block and the second prediction block to obtain a final prediction block of the current image block. The method increases the accuracy of the obtained prediction block of the current image block without increasing the code rate.

Abstract: A coding method based on multi-hypothesis motion compensation for a P-frame, including: a) using neighboring coded image blocks as reference image blocks, adopting a motion vector of each reference image block as a first motion vector which points to a first prediction block; b) adopting the first prediction block corresponding to each reference image block as a reference value, and performing joint motion estimation on the current image block to acquire a second motion vector which points to a second prediction block; c) weighted averaging the first prediction block and the second prediction corresponding to each reference image block to acquire a third prediction block of the current image block, respectively; and d) calculating a coding cost corresponding to each reference image block to determine a final first motion vector, a final second motion vector, and a final prediction block of the current image block.

Abstract: A content server comprising a processor coupled to the receiver configured to obtain a media content component, identify an ownership of the media content component, and generate a Dynamic Adaptive Streaming over Hypertext Transfer Protocol (HTTP) (DASH) Media Presentation Description (MPD) indicating storage locations of segments of the media content component, indicating instructions for displaying the segments of the media content component, and indicating ownership of the media content component, a receiver coupled to the processor and configured to receive a request from a client for the media content component, and a transmitter coupled to the processor and configured to transmit the DASH MPD indicating the ownership of the media content component to the client in response to receiving the request.

Abstract: A coding and decoding method for images or videos is provided by embodiments of the present invention to improve coding and decoding efficiency. The method includes: establishing a visual dictionary, wherein, the visual dictionary includes one or more visual words; extracting features from a specific object in an image; determining whether there is a visual word in the visual dictionary matching the specific object by using a feature matching method; obtaining the index of the visual word matched and a geometric relationship between the specific object and the visual word matched, wherein, the geometric relationship is represented by a project parameter; entropy coding the index of the visual word matched and the project parameter instead of entropy coding the specific object.

Abstract: The present invention provides a method and a device for detecting interest points in an image. The method includes: acquiring an original input image; performing down-sampling processing on the original input image, so as to obtain a plurality of sampling images with different resolutions; dividing each sampling image into a plurality of small image blocks; performing filtering processing on the plurality of small image blocks in each sampling image in sequence by using Laplacian-of-Gaussian filters, so as to obtain filtered images of the plurality of small image blocks in each sampling image; and acquiring interest points in an image in filtered images of the plurality of small image blocks in each sampling image. The present invention is used for solving the problems of more memory consumption and a low detection speed in the prior art.

Abstract: A media decoding method based on cloud computing and decoder thereof are provided by embodiments of the present invention, which are easy to use and applicable to a media of any form, and its requirement for computer resource is low. The method includes: extracting representing features from a media code stream to be decoded; searching in the cloud for a media object which has similar representing features with the media code stream to be decoded by using a feature matching method and the representing features extracted; filling, replacing and improving parts or segments of the media code stream to be decoded with whole or parts of the media object.

Abstract: A coding and decoding method for images or videos is provided by embodiments of the present invention to improve coding and decoding efficiency. The method includes: establishing a visual dictionary, wherein, the visual dictionary includes one or more visual words; extracting features from a specific object in an image; determining whether there is a visual word in the visual dictionary matching the specific object by using a feature matching method; obtaining the index of the visual word matched and a geometric relationship between the specific object and the visual word matched, wherein, the geometric relationship is represented by a project parameter; entropy coding the index of the visual word matched and the project parameter instead of entropy coding the specific object.