The invention provides methods that improve image compression and/or quality within the JPEG process by using a low-pass filter to remove high frequency components from image data, which removes blocking artifacts. Preferred embodiments apply the low-pass filter to the Chroma components after decompression prior to conversion into RGB color space.

The invention provides methods that improve image compression and/or quality within the JPEG process by using a low-pass filter to remove high frequency components from image data, which removes blocking artifacts. Preferred embodiments apply the low-pass filter to the Chroma components after decompression prior to conversion into RGB color space.

In one example, the present disclosure describes a device, computer-readable medium, and method for enabling secure video sharing by exploiting data sparsity. In one example, the method includes applying a transformation to a video dataset containing a plurality of video samples, to produce a plurality of sparse vectors in a first dimensional space, wherein each sparse vector of the plurality of sparse vectors corresponds to one video sample of the plurality of video samples, and multiplying each sparse vector of the plurality of sparse vectors by a transformation matrix to produce a plurality of reduced vectors in a second dimensional space, wherein the dimension of the second dimensional space is smaller than a dimension of the first dimensional space, and wherein the plurality of reduced vectors in the second dimensional space hides information about the video dataset while preserving relational properties between the plurality of video samples.

In one example, the present disclosure describes a device, computer-readable medium, and method for enabling secure video sharing by exploiting data sparsity. In one example, the method includes applying a transformation to a video dataset containing a plurality of video samples, to produce a plurality of sparse vectors in a first dimensional space, wherein each sparse vector of the plurality of sparse vectors corresponds to one video sample of the plurality of video samples, and multiplying each sparse vector of the plurality of sparse vectors by a transformation matrix to produce a plurality of reduced vectors in a second dimensional space, wherein the dimension of the second dimensional space is smaller than a dimension of the first dimensional space, and wherein the plurality of reduced vectors in the second dimensional space hides information about the video dataset while preserving relational properties between the plurality of video samples.

A system implements a hybrid coding mode. The hybrid coding mode may implement a transform to decompose an input stream into frequency components. The frequency components may include frequency bands such as those resulting from a wavelet transform. The frequency components may have associated coefficients which may be determined via the transform. The hybrid coding mode may also implement a predictor-based coding mode. A predictor-based coding mode uses a set of values as predictors for another set of values. The hybrid mode may be implemented by using predictor-based coding to code a portion of the coefficients. For example, a coefficient may be used as a predictor for another coefficient of same frequency component. In some implementations, dynamic selection between a hybrid coding mode and a point coding mode may be used.

A system implements a hybrid coding mode. The hybrid coding mode may implement a transform to decompose an input stream into frequency components. The frequency components may include frequency bands such as those resulting from a wavelet transform. The frequency components may have associated coefficients which may be determined via the transform. The hybrid coding mode may also implement a predictor-based coding mode. A predictor-based coding mode uses a set of values as predictors for another set of values. The hybrid mode may be implemented by using predictor-based coding to code a portion of the coefficients. For example, a coefficient may be used as a predictor for another coefficient of same frequency component. In some implementations, dynamic selection between a hybrid coding mode and a point coding mode may be used.

There is provided an image processing device and a method that are configured to be capable of reducing the increase of a load on encoding/decoding of attribute information of a point cloud. The attribute information of the point cloud is encoded using contexts corresponding to weight values obtained by an orthogonal transformation made on location information of the point cloud, the orthogonal transformation taking into consideration a three-dimensional structure. Further, encoded data associated with the attribute information of the point cloud is decoded using contexts corresponding to weight values obtained by an orthogonal transformation made on the location information of the point cloud, the orthogonal transformation taking into consideration a three-dimensional structure. The present disclosure can be applied to, for example, image processing devices, electronic equipment, image processing methods, programs, and the like.

A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. Hash elements corresponding to one or more neighboring nodes associated with a current node are identified. A size of a hash table is decreased based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes. The data corresponding to the point cloud is decoded based on the hash table having the decreased size.

A method, computer program, and computer system is provided for decoding point cloud data. Data corresponding to a point cloud is received. Hash elements corresponding to one or more neighboring nodes associated with a current node are identified. A size of a hash table is decreased based on deleting one or more of the hash elements corresponding to non-border regions of the one or more neighboring nodes. The data corresponding to the point cloud is decoded based on the hash table having the decreased size.

Using the techniques discussed herein, a set of images is captured by one or more array imagers (106). Each array imager includes multiple imagers configured in various manners. Each array imager captures multiple images of substantially a same scene at substantially a same time. The images captured by each array image are encoded by multiple processors (112, 114). Each processor can encode sets of images captured by a different array imager, or each processor can encode different sets of images captured by the same array imager. The encoding of the images is performed using various image-compression techniques so that the information that results from the encoding is smaller, in terms of storage size, than the uncompressed images.