1980 results about "Coding decoding" patented technology

A method and device for encoding, decoding, storage and transmission of a scalable data stream to include layers having different coding properties including: producing one or more layers of the scalable data stream, wherein the coding properties include at least one of the following: Fine granularity scalability information; Region-of-interest scalability information; Sub-sample scalable layer information; Decoding dependency information; and Initial parameter sets, and signaling the layers with the characterized coding property such that they are readable by a decoder without the need to decode the entire layers. A corresponding method of encoding, decoding, storage, and transmission of a scalable bit stream is also disclosed, wherein at least two scalability layers are present and each layer has a set of at least one property, such as those above identified.

In some embodiments, techniques for data security may include encoding and decoding unreadably encoded data, such as data encrypted with a public key or tokenized. In some embodiments, techniques for data security may include distributing an encrypted private key. In some embodiments, unreadable data may be encrypted and/or decrypted using time-varying keys.

In some embodiments, techniques for data security may include combining information and a policy, and encoding the combined information and policy, wherein encrypting the combined information and policy is performed using a public key, or via tokenization.

In some embodiments, techniques for data security may include receiving data, wherein the data has been encoded, decoding the data, determining a first datum and a second datum, wherein the first datum and the second datum are associated with the decoded data, and determining a policy, wherein the policy is associated with the first datum.

The invention provides an unsupervised domain-adaptive brain tumor semantic segmentation method based on deep adversarial learning. The method comprises the steps of deep coding-decoding full-convolution network segmentation system model setup, domain discriminator network model setup, segmentation system pre-training and parameter optimization, adversarial training and target domain feature extractor parameter optimization and target domain MRI brain tumor automatic semantic segmentation. According to the method, high-level semantic features and low-level detailed features are utilized to jointly predict pixel tags by the adoption of a deep coding-decoding full-convolution network modeling segmentation system, a domain discriminator network is adopted to guide a segmentation model to learn domain-invariable features and a strong generalization segmentation function through adversarial learning, a data distribution difference between a source domain and a target domain is minimized indirectly, and a learned segmentation system has the same segmentation precision in the target domain as in the source domain. Therefore, the cross-domain generalization performance of the MRI brain tumor full-automatic semantic segmentation method is improved, and unsupervised cross-domain adaptive MRI brain tumor precise segmentation is realized.

A novel bandwidth extension technique allows information to be encoded and decoded using a fractal self similarity model or an accurate spectral replacement model, or both. Also a multi-band temporal amplitude coding technique, useful as an enhancement to any coding/decoding technique, helps with accurate reconstruction of the temporal envelope and employs a utility filterbank. A perceptual coder using a comodulation masking release model, operating typically with more conventional perceptual coders, makes the perceptual model more accurate and hence increases the efficiency of the overall perceptual coder.

A lossless image encoding/decoding method and apparatus. The lossless color image encoding apparatus includes a motion prediction image generator estimating a motion between a previous image and a current image and outputting a corresponding prediction image, a residue generator generating a temporal residue corresponding to a difference between a prediction image generated by the motion prediction image generator and the corresponding block of the current image with respect to different components of the color image, a prediction residue generator generating prediction residues by defining a linear-transformed value of one residue among the different components of the color image output from the residue generator as a predictor and using differences between each of the residues of the other components and the predictor, and an entropy encoder receiving the residue forming the predictor from the residue generator and the prediction residues from the prediction residue generator and generating a bitstream. Encoding methods, decoding apparatuses, and decoding methods can be implemented similarly.

The invention relates to a polarization code decoding method for cyclic redundancy check assistance. When a polarization code is decoded, in all the routes with cyclic redundancy check values of corresponding bit estimation sequences of being zero from a root node to leaf nodes on a code tree corresponding to the polarization code, one route with maximum reliability metric value is searched by taking a list or stack as assistance for route search, and the bit estimation sequence corresponding to the route is output as a decoding result. The method comprises the following operation steps of: determining parameters according to a search assistance method, constructing an auxiliary structure of the decoding method, searching a candidate bit estimation sequence and executing cyclic redundancy check. By adopting the method disclosed by the invention, error correcting capability of a communication system which adopts the polarization code as channel coding is greatly improved, operation steps are simpler, and operation complexity is equivalent to or even lower than that of a Turbo code coding and decoding method used in a WCDMA (wideband code division multiple access) system, thus the method disclosed by the invention has a good practical prospect.

An image signal output device includes a code generating means for generating a two-dimensional geometrical code based on a source data, a obtaining means for retrieving a sequential image signal, a code inserting means for inserting the two-dimensional geometrical code in the sequential image signal and a output means for outputting the sequential image signal. The code inserting means imperceptibly inserts the two-dimensional geometrical code in the sequential image signal, and the output means outputs the sequential image signal having the two-dimensional geometrical code inserted therein. The imperceptible two-dimensional geometrical code is captured by a camera in a cell phone to be decoded and stored in a memory medium.

A prediction encoder/decoder, a prediction encoding/decoding method, and a computer readable recording medium having a program for the prediction encoding/decoding method recorded thereon. The prediction encoder includes a prediction encoding unit that starts prediction at an origin macroblock of an area of interest of a video frame, continues prediction in an outward spiral in the shape of square rings composed of macroblocks surrounding the origin macroblock, and encodes video by performing intra-prediction using information about a macroblock that has just been coded in a square ring including a macroblock to be coded and a macroblock in a previous square ring and adjacent to the macroblock.

Provided is a method for decoding video, which performs prediction in an intra mode. The method for decoding video according to the invention comprises steps of: creating a current table index using a code number and MPM index information; and deriving a current prediction mode by adopting an index mapping table in the current table index. According to the present invention, video encoding/decoding efficiency can be improved.

The invention discloses a text summarization generation system and method based on coding-decoding deep neural networks. The system comprises: an Internet text obtaining module which is used for obtaining text information on the Internet; a data preprocessing module which is used for preprocessing the text information; a summarization model training module which is used for extracting quantified text information from the preprocessed text information and performing training according to a coding-decoding deep neural network model to obtain a summarization training model; a summarization generation module which is used for using the preprocessed text information as input and outputting summarization information with preset length according to the coding-decoding deep neural network model. The text summarization generation system and method based on coding-decoding deep neural networks have following advantages that text information is compressed into a summarization text with a coherent description by means of computer automatic analysis and abstraction or generation of the central content expressed by the text, which facilitate users to understand the text content and then to quickly read and select information of interest; the text is compressed into a summarization to reduce the browse burden of users.

A machine readable code, a code encoding method and device and a code decoding method and device are provided. This machine readable code includes a data area made up of at least one data cell, in which different colors or shades are encoded and expressed depending on the content of the information, and a parity area made up of at least one parity cell, the parity area provided to determine whether colors or shades expressed in the data cells have been properly expressed depending on the content of the information. Since this code image includes a parity area for parity inspection, mal-recognition of colors due to differences between input devices such as cameras or between the environments such as light can be easily detected and corrected.

An audio decoding device is provided for decoding N_A (where N_A>1) channels of audio signals by a sub-band synthesis operation using sub-band synthesis filter data and sub-band signal data. The decoding device includes a first memory section for storing M_A (where M_A<N_A) channels of the sub-band synthesis filter data and the sub-band signal data, a second memory section for storing at least some of N_A channels, an operation section for receiving encoded audio data and decoding the encoded audio data into sub-band signal data, and a data transfer section for, switching, by M_A channels, the sub-band synthesis filter data and the sub-band signal data in the first memory section and the second memory section.

An STBC encoding extension method for more than two transmit antennas, which provides higher diversity gains while keeping the same coding/decoding latency as in the two-transmit-antenna case of conventional STBC encoding. A N×2 STBC encoder is constructed from a 2×2 STBC encoder, wherein the N×2 STBC encoder is suitable for transmission with higher numbers of transmit antennas including wireless transmission systems with N×1 antenna configurations where N>2.

A remote sensing image segmentation method combining complete residual and multi-scale feature fusion includes S100 improving a convolution coding-decoding network as a segmentation backbone network,separately comprising S101 using the convolution coding-decoding network as the segmentation backbone network; S102 adding a feature pyramid module for aggregating multi-scale context information intothe backbone network; S103 adding a residual unit into the convolution layer corresponding to the encoder and the decoder of the backbone network, and meanwhile, fusing the features in the encoder into the corresponding layer of the decoder in a pixel-by-pixel manner; S200 using the improved image segmentation network combined with complete residual and multi-scale feature fusion for remote sensing image segmentation; S300 outputting the segmentation result of the remote sensing image. This method not only simplifies the training of the deep network and enhances the feature fusion, but also enables the network to extract rich context information, cope with changes in the scale of the target, and improve the segmentation performance.

An encoder transforms at least a portion of a signal, counts the resulting transform coefficients having a zero value, and encodes the signal with the zero count. A decoder decodes the signal in order to recover the zero count. The decoder may also determine its own zero count of the signal as received and may compare the zero count that it determines to the recovered zero count. The decoder may be arranged to detect compression/decompression based upon results from the comparison, and/or the decoder may be arranged to prevent use of a device based upon results from the comparison.

The present invention relates to a video encoding/decoding method and apparatus. The video decoding method according to the present invention may comprise dividing a coding unit into a plurality of sub-units according to a block division structure and performing decoding based on the sub-unit, wherein the block division structure is a block division structure in which division is performed so as to include at least one sub-unit having a predetermined block form other than square and rectangle forms.

A moving picture decoding method according to the present invention includes: performing intra prediction on a target block to calculate values of predicted samples of the target block; and calculating reconstructed samples of the target block by adding difference data and the values of the predicted samples of the target block, wherein in the performing of intra prediction, validity of each of reference samples that are located one of immediately above and immediately to the left of the target block is determined, and when the reference samples include both a valid reference sample and an invalid reference sample, the intra prediction is performed using the valid reference sample, and an intra-predicted reference sample is determined as a valid reference sample, and an inter-predicted reference sample is determined as an invalid reference sample.

A video encoding/decoding method and apparatus is disclosed that can efficiently compress/decompress motion vectors in a video codec including a hierarchical temporal level decomposition process. The video encoding method including a hierarchical temporal level decomposition process involves obtaining a predicted motion vector of a second frame, which exists at a present temporal level, from a first motion vector of a first frame that exists at a lower temporal level; obtaining a second motion vector of the second frame by performing a motion estimation in a predetermined motion search area using the predicted motion vector as a start point; and encoding the second frame using the obtained second motion vector.

A point of sale (POS) bar code scanner such as in common use in retail stores includes provision for reading bar coded redemption coupons (or other bar coded documents redeemable for value) in a manner secure from a human operator. Integration of functions between the POS scanner and the coupon reader varies with different embodiments described. Through software associated with bar code decoding logic, a comparison is made between information on a validation coupon and information on items presented for purchase, and a decision is made as to whether the redemption coupon is valid and redeemable in this transaction. In one embodiment, the bar code scanner simply receives the redemption coupon face down on the product scanner window, and integrated decode logic of the scanner identifies the bar code as belonging to a redemption coupon, then makes the comparison and validation of the coupon for the particular transaction. In some embodiments of the invention, the POS product scanner has a special beam exit window through which the scanning beam is diverted when it is signified that a redemption coupon is to be read, with a coupon reading scan pattern. After validation the coupon may be punched or otherwise cancelled, and/or sent to a secure container. Several types of securing devices are disclosed, for preventing subsequent use of redeemed coupons.

A method and a device for encoding/decoding video signals by motion compensated temporal filtering. Blocks of a video frame are encoded/decoded using temporal and spatial correlations according to a scalable Motion Compensated Temporal Filtering (MCTF) scheme. When a video signal is encoded using a scalable MCTF scheme, a reference block of an image block in a frame in a video frame sequence constituting the video signal is searched for in temporally adjacent frames. If a reference block is found, an image difference (pixel-to-pixel difference) of the image block from the reference block is obtained, and the obtained image difference is added to the reference block. If no reference block is found, pixel difference values of the image block are obtained based on at least one pixel adjacent to the image block in the same frame. Thus, the encoding procedure uses the spatial correlation between image blocks, improving the coding efficiency.

The invention discloses an image semantic segmentation method based on a pyramid pooled coding-decoding structure. The method comprises: an inputted image is processed by a coding network including aconvolutional neural network model and a pyramid pooling model, high-dimensional feature information of the inputted image is extracted, and a deep feature map is formed; the deep feature map is restored to one with the resolution ratio identical with that of the inputted image by a decoding network including an inverse convolutional neural network model; and a feature map outputted by the decoding network is classified by using a classifier including a convolutional layer with a class number of output channels and a Softmax layer, thereby realizing image semantic segmentation. With the methoddisclosed by the invention, the capability of image semantic segmentation by the network is enhanced and the network model size and the operation speed are both considered.