101 results about "Lossless coding" patented technology

This application discloses a lossless encoding method and apparatus, a lossless decoding method and apparatus, and a device, pertaining to the field of encoding and decoding technologies. The lossless decoding method in embodiments of this application includes: traversing, by an encoder side, a target mesh and obtaining geometry coordinates of each vertex in the target mesh; determining, by the encoder side, vertices with same geometry coordinates as duplicate vertices; merging, by the encoder side, the duplicate vertices in the target mesh to obtain a first mesh; and performing, by the encoder side, lossless encoding on the first mesh to generate a target bitstream.

The invention relates to a three-dimensional reconstruction-oriented Gaussian point cloud lossless coding and decoding method, a product and a storage medium. The method comprises the following steps: extracting a first Gaussian point cloud corresponding to anhor data in a numpy array format at a coding side; converting the first Gaussian point cloud in the numpy array format into a first Gaussian point cloud in a ply format; meanwhile, through a Morton code-based spatial sorting method, performing Morton code sorting on geometric data and attribute data of the first Gaussian point cloud in the numpy array format, and generating a Morton sequence index table; and carrying out AVS coding on the first Gaussian point cloud in the ply format to obtain compressed data in a binary code stream form. On the decoding side, AVS decoding is carried out when the compressed data in the binary code stream form and the Morton sequence index table are received, and a second Gaussian point cloud in the ply format is obtained through reduction; performing format conversion on the second Gaussian point cloud in the ply format to obtain a second Gaussian point cloud in a numpy array format; and rearranging the attribute data and the geometric data based on the Morton sequence index table to realize one-to-one correspondence of the geometric data and the attribute data between the first Gaussian point cloud and the second Gaussian point cloud before and after coding and decoding. Compared with an existing method, the data consistency before and after coding is improved.

The invention relates to an array microphone noise reduction recording method based on cascade noise reduction and blind source separation, which belongs to the technical field of voice signal processing and recording, and comprises the following steps: configuring a multi-channel array microphone, ensuring that the amplitude and phase of a channel signal are consistent, and collecting an original multi-channel voice signal; a weighted kernel function blind source separation algorithm is adopted, signal-to-noise ratio distribution characteristics of signals are extracted, kernel function weights are given, and target voice and interference signal components are obtained through decoupling of an independent component analysis model; executing target-oriented adaptive cascade noise reduction, locking the voice of a keynote speaker through directional pickup, reducing noise, filtering out reverberation, and enhancing the voice of a far-field target by combining a voice mask neural network with a far-field pickup algorithm in sequence; and processing the target voice through voice feature perception lossless coding and storing the target voice. According to the invention, stable acquisition of multi-channel signals, accurate separation of mixed signals and layered suppression of noise reverberation are realized, the signal-to-noise ratio and definition of far-field voice are significantly improved, and the method is suitable for single-person speaking or multi-person dialogue scenes.

The invention discloses a medical image lossless compression method and system based on transform coding and deep learning, and the method comprises the steps: carrying out the DCT and Round quantization of an input 2D medical image, and dividing the image into a DC coefficient and an AC coefficient; the loss R caused by the DC coefficient and Round quantization is compressed; decomposing the AC coefficient into a structural layer MSB and a texture layer LSB; decomposing the texture layer LSB into a plurality of sub-coefficient blocks; constructing a depth probability estimation model based on a convolutional neural network, carrying out grouping-based autoregression probability density modeling, inputting a plurality of sub-coefficient blocks, and outputting a probability distribution matrix of each sub-coefficient block; and performing lossless coding and decoding on each sub-coefficient block in combination with an entropy arithmetic encoder and a probability distribution matrix. The method has the advantages of high compression efficiency, lossless compression and the like.

The invention discloses a GPU shader rendering computer implementation method of a finite element result. The method comprises the steps that a static three-dimensional grid is adopted as a rendering geometry; the 32-bit floating point type scalar data corresponding to the vertexes are subjected to lossless coding at a CPU end to obtain four-channel 8-bit RGBA vertex color attributes; during data updating, lightweight vertex color data are only transmitted to the GPU; hardware interpolation is carried out on the encoded color attributes by using a GPU rasterizer; and finally, in the fragment shader, decoding the interpolation result of each pixel to reconstruct a scalar value, and mapping the scalar value into a final color. According to the method, the bottleneck of topological calculation of a CPU end and transmission of massive geometric data to the GPU is avoided, the calculation load is transferred to the GPU in a large scale for parallel processing, the rendering efficiency is remarkably improved, and high-frame-rate dynamic visualization of a finite element result is realized.

The invention discloses an efficient video stream transmission and management platform based on a message queue, and relates to the technical field of video stream transmission. After receiving a transmission instruction, the platform collects and stores operation parameters of video equipment in real time to monitor whether the equipment state is healthy or not, obtains an original video stream from the accessed video equipment, and transmits the original video stream to the video equipment; the method comprises the steps of generating a comprehensive judgment index through first judgment analysis to judge a network condition, transcoding an original video stream to generate a video multi-layer resolution slice, constructing a lossless coding context model based on the video multi-layer resolution slice through second judgment analysis, generating a high sub-band primer set and a low sub-band primer set, and generating a high sub-band primer set and a low sub-band primer set. Message units with priorities are constructed to facilitate transmission and management of video streams; according to the method, the security of image transmission can be enhanced, efficient lossless compression under multiple resolutions is also realized, efficient transmission is ensured, and particularly, intelligent optimization of video transmission is realized in a scene with limited network resources.

The invention discloses a two-stage pulse data lossless coding method and device based on lossy coding and residual coding. Through a deep neural network, the preprocessed pulse data are used as input, probability distribution of latent variables and residual errors is obtained through two stages of lossy coding and residual error coding, and therefore corresponding code stream files are output, and the purpose of lossless compression of the pulse data is achieved. The existing coding model is not suitable for processing pulse data or is difficult to realize lossless coding; according to the method, a first pulse data lossless coding model is adopted, a bit splicing data preprocessing mode is adopted, so that the neural network is easier to mine data correlation, and a two-stage framework of lossy coding and residual coding is applied to ensure lossless coding. On the basis of the mode, the neural network coding model can be integrally optimized by utilizing an end-to-end training method, so that better pulse data lossless coding performance is obtained.

The invention discloses an image coding method and system based on a generalized Gaussian model, and the related method comprises a coding part and a decoding part, the coding part comprises transformation, quantization and entropy coding, the decoding part comprises entropy decoding, inverse quantization and inverse transformation, and for image lossy coding, the inverse quantization and inverse transformation are carried out. Introducing a generalized Gaussian model to carry out modeling on probability distribution of the feature map in the encoding process, and carrying out entropy encoding and entropy decoding on the feature map according to generalized Gaussian model parameters obtained through modeling; for image lossless coding, a generalized Gaussian model is introduced to carry out modeling on probability distribution of pixels or lossless transformation coefficients in image lossless compression, and entropy coding and entropy decoding are carried out according to generalized Gaussian model parameters obtained through modeling. According to the scheme provided by the invention, the generalized Gaussian model is introduced into an image coding scheme, so that more accurate probability estimation can be carried out, the code rate required by image compression is saved, the compression performance is improved, and relatively low complexity is maintained.

The invention relates to the field of image lossless coding, and provides an enhanced QOI encoder realized based on an FPGA. The design is based on an enhanced QOI encoding strategy, and the encoder consists of an input bit width conversion module, a JPEG-LS predictor, an enhanced QOI encoding unit, an encoding group FIFO module, a compact bit width conversion module and a keep signal specification module, and has the characteristics of higher throughput rate, good time sequence performance and lower hardware resource consumption. A core component JPEG-LS predictor and an enhanced QOI coding unit adopt pipeline design, and two pixels can be coded in each clock period; coding of images with any resolution ratio is supported (only the image width is required to be an even number); 64-bit AXI Stream bus interfaces are adopted for input pixel streams and output coded data streams, and coded data output does not carry null bytes, so that DMA (direct memory access) transmission is facilitated.

A method and an apparatus for switching between a lossy codec and a lossless codec are provided. The method includes: obtaining a waveform of a previous frame Ti−1, and updating the waveform of the previous frame Ti−1 to an input frame buffer in the lossless codec, where the waveform of the previous frame Ti−1 is encoded by a lossy encoder; performing integer window time domain aliasing cancellation INT winTDAC on a waveform in a lossless encoder buffer to obtain a first transform result, and updating the first transform result to an overlapped buffer in the lossless codec; obtaining a waveform of a current frame Ti, and updating the waveform of the current frame Ti to the input frame buffer; and performing integer modified discrete cosine transform INTMDCT on the waveform in the lossless encoder buffer to obtain a second transform result.

The present application discloses a data compression method and system based on a hybrid compression algorithm, which relates to the field of compression of time series measurement data. The method includes: obtaining a measured monitoring parameter sequence and historical measured monitoring seed data, and calling a prediction model to determine a predicted monitoring parameter sequence, and calculating a residual sequence; dividing the residual sequence into a low-variance segment and a high-variance segment according to the standard deviation of the residual sequence in each sliding window; performing clustering processing on the residual values of the low-variance segment to generate corresponding dictionary indexes, and performing frequency-domain transformation, quantization processing and entropy coding on the residual values of the high-variance segment to generate corresponding frequency-domain compression codes; performing lossless coding on the historical measured monitoring seed data to generate corresponding seed data compression codes, and obtaining measured monitoring compressed data for the measured monitoring parameter sequence through packing and combination. Thus, a hybrid compression algorithm is introduced at the edge device coding end, and the dependence on the full-scale analysis of real-time data is reduced through prior knowledge modeling.

The present invention provides a three-dimensional point cloud encoding method, a three-dimensional point cloud decoding method, a device, and an electronic device, belonging to the field of artificial intelligence technology. The encoding method includes: downsampling an original point cloud according to a downsampling parameter to obtain a downsampled point cloud and target information of each point in the downsampled point cloud, where the downsampling parameter is a non-integer greater than 1; determining neighbor occupancy information of each point in the downsampled point cloud; clustering the downsampled point cloud according to the downsampling parameter to obtain point cloud clustering clusters; training an interpolation network for each clustering cluster according to the target information and neighbor occupancy information of each point in each point cloud clustering cluster to obtain optimized interpolation network parameters for each clustering cluster; performing lossless encoding on the downsampled point cloud to obtain downsampled point cloud encoded data, and encoding the interpolation network parameters of each clustering cluster to obtain network encoded data. In this way, it can effectively support the point cloud encoding of fractional downsampling and achieve more flexible and efficient point cloud compression.

The present disclosure provides systems and methods for performing residual encoding of video data. According to certain disclosed embodiments, the method comprises: receiving control information for encoding a video sequence; and determining an encoding method for encoding a prediction residual signal of an encoding block in the video sequence based on the control information. The coding method is one of transform residual coding and transform skip residual coding.

The present disclosure provides systems and methods for performing residual encoding of video data. According to certain disclosed embodiments, the method comprises: receiving control information for encoding a video sequence; and determining an encoding method for encoding a prediction residual signal of an encoding block in the video sequence based on the control information. The coding method is one of transform residual coding and transform skip residual coding.

In end-to-end compression, an image may be encoded using a deep neural network-based encoder. The embedding output from the encoder is quantized and encoded using a lossless encoder. In one embodiment, we suggest to apply uniform VQ (vector quantization) to embedding trained / optimized for uniform SQ (scalar quantization). In addition, we suggest to employ an existing model and to fine-tune a portion of the model or the entire model according to a new fixed optimal VQ graph. In this manner, the decoder becomes more stable for higher individual quantization errors. Because the encoder can be trained based on SQ and deployment is based on VQ, our method is also referred to as a hybrid SQ / VQ method.

The present disclosure provides systems and methods for performing residual coding of video data. According to certain disclosed embodiments, the methods include: receiving control information for coding a video sequence; and determining, based on the control information, a coding method for coding a prediction residual signal of a coding block in the video sequence. The coding method is one of a transform residual coding and a transform-skip residual coding.

The embodiment of the invention relates to an image encoding apparatus and an image decoding apparatus. An encoded semantically segmented image of an input image is generated based on a semantic segmentation deep neural network (120) and a lossless encoder (130). An encoded coarse image of the input image is generated based on a first lossy encoder (140) and the semantic-based distortion map. An estimated image of the input image is generated based on a semantic segmented image in a fine deep neural network (150), a coarse image, and the semantic-based distortion map. Based on the estimated image, the input image and a second lossy encoder (160) generate an encoded residual image. Furthermore, embodiments of the invention also relate to a first communication device and a second communication device, and also to corresponding methods and computer programs.

Disclosed by the present invention is a lossless coding and decoding method for reducing a video transmission bandwidth, thus improving real-time performance of a system. In addition, A floating-point number from probability calculation is prevented from affecting the coding. Two-stage compression improves the compression ratio with simple and practical operations. The present invention greatly reduces the bandwidth resources occupied in the video transmission process, thus having extremely high application value in the occasions with requirements for high-quality video image.

Methods and apparatuses are provided for video coding with lossless coding modes. The method includes: determining whether a current block is in a transform skip mode; in response to determining that the current block is in the transform skip mode, determining whether a first residual coding scheme or a second residual coding scheme is selected for the current block in the transform skip mode; and signaling a first indication which indicates whether a current block is in the transform skip mode and signaling a second indication which indicates whether the first residual coding scheme or the second residual coding scheme is applied for blocks in the transform skip mode.

The decoder includes circuitry configured to receive a bitstream in which a current frame is identified, wherein the current frame includes a first region and a third region, detect in the bitstream an indication that the first region is encoded according to a lossless encoding protocol, and decode the current frame, wherein decoding the current frame further includes decoding the first region using a lossless decoding protocol corresponding to the lossless encoding protocol.

A method for processing a prediction unit (PU) to generate predicted samples is provided that includes computing predicted samples for samples of the PU using sample-based angular intra-prediction (SAP) when lossless coding is enabled for the PU, and computing predicted samples for the samples of the PU using block-based angular intra-prediction when lossless coding is not enabled for the PU. Computation of the predicted using SAP includes determining an intra-prediction angle for the PU, and computing a predicted sample for each sample of the samples in the PU based on linear interpolation of two reference samples adjacent to the sample, wherein the two reference samples are selected according to the intra-prediction angle.

The application provides a mass medical image cold data joint compression method, a feature extraction network is used for fine-grained feature extraction on large-scale medical images, a feature database of medical images is established, and the extracted image features are stored according to classification; the feature extraction network is used for the medical image to be compressed, features are extracted and similarity matching is performed; then, based on image brightness, slices of the reference image and the image to be compressed are sorted to optimize the compression order; finally, according to the reference image, lossy coding of the current image is assisted, and according to the original image, lossless coding of the current image is assisted.

The invention discloses a high-precision wave recording acquisition and time mark synchronization system for power grid fault positioning, and relates to the technical field of power grid wave recording synchronization, and the system comprises the steps: 1, synchronously outputting three-gear sampling under a time axis of the same coordinated world time, and generating a time uncertainty upper bound and a group delay signature for a channel and a fragment; 2, performing quality locking near-lossless on the high-energy fragment, after coding, checking the high-energy fragment to reach a sample position and a peak amplitude through homologous detection, and returning to be lossless if the high-energy fragment is unqualified; 3, writing a fragment index, quality metadata, a fragment verification structure and a whole file signature into the compatible single file expansion container; step 4, carrying out progressive retrieval, breakpoint resume and cross-station de-duplication according to indexes, and implementing cold and hot hierarchical storage; and 5, based on group delay signature compensation and quality weighted loading, solving a reference moment and generating an acceptance check list. According to the scheme, cross-device alignment, reproducible key features, transmission and storage peak clipping, playback and evidence obtaining caliber unification and tool compatibility and auditing are realized.

An encoder, a decoder, a method and a computer program for improved lossless compression are disclosed. The decoder is configured to determine, for a predetermined portion of a picture, whether to encode the predetermined portion of the picture into the data stream using lossless or lossy encoding, and to encode the predetermined portion of the picture into the data stream if the predetermined portion of the picture is using lossless encoding. If so, decoding a prediction residual for a predetermined portion from the data stream using binarization of the prediction residual data to the binary value string and context-adaptive binary entropy decoding of the binary value string in a first manner; and if lossy coding is used, decoding a prediction residual for a predetermined portion from the data stream using binarization of prediction residual data into a binary value string and context-adaptive binary entropy decoding of the binary value string in a second manner, where the first manner is different from the second manner, computing complexity is reduced in the first mode compared with that in the second mode.

The invention discloses a lossless coding model fine tuning method and device, equipment, a medium and a product, and relates to the field of image processing, and the method comprises the steps: decomposing the weight of a linear layer and each network layer in a lossless coding model in a fine tuning stage, and determining the increment weight of the linear layer and each network layer; on the basis of a progressive fine tuning method guided by a code rate, performing fine tuning on the pre-trained lossless coding model on the training image, quantifying an increment weight, and determining a loss function of the pre-trained lossless coding model; on the basis of the loss function, according to the quantized increment weight and a pre-trained weight, determining a weight after fine tuning, and reconstructing the pre-trained lossless coding model; according to the reconstructed lossless coding model, the test image is coded into the bit stream, the decoder decodes the increment weight of the reconstructed lossless coding model, and the decoded image is determined, the calculation amount is reduced, and the performance of the pre-training model on the test set of the out-of-domain image is improved.

The invention discloses an image coding and decoding display method and system, relates to the technical field of electronic information, and can determine an image needing to additionally transmit a quality enhanced data packet by additionally considering the operation logic of a user on a cloud desktop terminal side and the refreshing frequency of the image in a cloud desktop image coding process, thereby ensuring the user experience. According to the specific scheme, a server side encodes a to-be-displayed image in an H264 lossy encoding mode and issues the to-be-displayed image to a cloud desktop terminal to be decoded and displayed. Meanwhile, a time threshold value T is set, and under the condition that the cloud desktop image is not refreshed after the time T, the server side constructs an image quality enhancement data packet of lossless coding and sends the image quality enhancement data packet to the cloud desktop terminal to be decoded and displayed. The server side sets a time threshold T according to the user input quantity uploaded by the cloud desktop terminal, and the larger the user input quantity is, the smaller the T is.

The application discloses a point of interest check-in sequence generation method based on a diffusion model, and belongs to the technical field of spatiotemporal data mining and deep learning. Data preprocessing is performed to clean and smooth a real world data set, so as to ensure data quality and consistency. Spatiotemporal lossless coding is performed to convert check-in sequences of different lengths into check-in vectors of equal lengths, and the original sequence information is retained by coding through a spatial frequency vector and a time bucket vector. A diffusion model is established to construct a spatial diffusion module and a time diffusion module, and spatiotemporal features are captured by using a forward diffusion process and a backward reconstruction process. A conditional U-shaped network is introduced into the diffusion module, and a denoising network is proposed to capture complex spatiotemporal correlations by modeling through a self-attention mechanism. A contrast learning strategy is used to further capture the spatiotemporal correlations of the check-in sequence by using ternary contrast learning, and the connection between the time and space diffusion modules is strengthened.

The invention provides an image coding transmission method and device based on a cache mechanism, equipment and a medium, relates to the technical field of computers, and solves the problem that the transmission bandwidth is wasted due to the fact that the same image content is coded for multiple times in the prior art. According to the scheme, the macro blocks subjected to lossless coding can be stored at the coding side by utilizing the cache mechanism, so that the macro blocks with the same image content do not need to be coded again in subsequent processing, the coding times of the same image content are effectively reduced, the coding efficiency of the image is further improved, and the coding efficiency of the image is improved. The code rate of cloud desktop hybrid coding is reduced, and the effect of saving bandwidth is achieved.

The invention discloses a covert communication device and method based on an offshore platform photoelectric monitoring system. The covert communication device comprises a sending end embedding module, a receiving end extraction module and a lossless transmission module. The sending end embedded module is used for receiving a video stream for monitoring the marine situation in real time and processing the video stream to obtain an image subjected to lossless coding compression; the receiving end extraction module is used for receiving the image subjected to lossless coding compression and processing the image subjected to lossless coding compression to obtain embedded information data; and the lossless transmission module is used for data communication between the sending end embedding module and the receiving end extraction module. According to the method, hidden transmission of hidden communication content is realized, existence of the hidden communication content is covered, the risk of secret leakage caused by the problems of hardware loss of the encryption equipment and the like is reduced, and maritime secret communication modes and methods are enriched.