89 results about "Rate distortion" patented technology

The invention provides a communication segmentation learning system and method for adaptive channel compression, and a medium, and relates to the technical field of segmentation learning and communication compression. The system comprises a plurality of clients, a server side and a channel compression device arranged between the clients and the server side, the channel compression device comprises a channel sensitivity modeling module, a rate distortion adaptive compression module and a cross-client fair coordination module. In a channel sensitivity modeling module, channel importance is dynamically evaluated through intermediate layer activation value fusion; differentiated quantization and compression strategies are designed on the basis of sensitivity scores in a rate-distortion self-adaptive compression module, so that key information is reserved while communication overhead is reduced; and meanwhile, the cross-client fair coordination module realizes balanced distribution of communication resources among multiple clients through a fairness regularization and dual optimization mechanism, so that the influence of excessive compression on global convergence is avoided. According to the method, the communication efficiency and the training stability of segmentation learning in a complex heterogeneous environment are remarkably improved.

The embodiment of the invention provides a Gaussian point cloud rendering method and device, equipment and a storage medium, and relates to the technical field of computer vision. The method comprises the following steps: acquiring pixel points of foreground targets in two corresponding key frames, generating a double-view-angle Gaussian point cloud based on the pixel points, quantifying double-view-angle Gaussian parameters of the double-view-angle Gaussian point cloud based on different rate-distortion coefficients to obtain quantized Gaussian parameters corresponding to each rate-distortion coefficient, and obtaining the target foreground target in the two key frames according to the quantized Gaussian parameters of the double-view-angle Gaussian point cloud and the quantized Gaussian parameters of the double-view-angle Gaussian point cloud. At least entropy coding is carried out on the quantized Gaussian parameter to obtain a coded Gaussian parameter; and determining a target rate-distortion coefficient according to the definition requirement, selecting a coding Gaussian parameter corresponding to the target rate-distortion coefficient as a target coding Gaussian parameter, and sending at least one target coding Gaussian parameter corresponding to each dual-view sequence to a decoding end. The frame extraction operation is performed on the video frame, so that the data volume can be reduced. Secondly, quantitative Gaussian parameters corresponding to different definitions are obtained according to different rate distortion coefficients, and balance of data transmission and rendering quality is achieved.

Embodiments of the present disclosure provide a solution for video processing. A method for video processing is proposed. The method comprises: determining, for a conversion between a video unit of a video and a bitstream of the video unit, whether to apply at least one neural network (NN) filter model or determine a rate distortion cost during a rate distortion optimization (RDO) process of the video unit based on at least one of: a distortion without NN filter model, a distortion with n-th NN filter model, a combination of distortions of a plurality of NN filter models, or coding statistics of the video unit, and wherein n is an integer number; determining a coding mode of the video unit based on a rate distortion optimization (RDO) criterion in the RDO process; and performing the conversion based on the coding mode.

The embodiment of the invention provides an end-to-end image compression method and system based on window local attention and generalized chessboard space channel context, and belongs to the technical field of image processing. The method comprises the following steps: constructing a transformation network based on an attention module and a stacked residual block; the transformation network based on the attention module and the stacked residual block is used for executing adaptive transformation of contents through dynamic representation and neighborhood information embedding to obtain potential features; establishing a generalized chessboard space channel context model; the generalized chessboard space channel context model is used for carrying out entropy coding on the potential features; and obtaining image compression data according to the transformation network based on the attention module and the stacked residual block and the generalized chessboard space channel context model. According to the method, redundancy can be eliminated to the maximum extent, excellent rate distortion performance is achieved, and meanwhile high-throughput parallel computing efficiency is ensured.

The invention discloses a video coding method based on multi-domain perception feature fusion, and belongs to the technical field of image communication. The method comprises the following steps: extracting perception features of a space domain, a time domain and a frequency domain from an input video frame sequence; normalizing the multi-domain sensing features, and generating a sensing importance factor for each coding unit through a dynamic fusion weight mechanism; constructing perceptual distortion evaluation based on perceptual importance factors, replacing a traditional rate distortion objective function, and guiding code rate distribution of coding units; and according to the perception importance factor, performing nonlinear mapping by using a hyperbolic tangent function, calculating to obtain a coding unit level quantization parameter adjustment amount, and obtaining a final quantization parameter in combination with the basic quantization parameter, thereby realizing adaptive rate distortion optimization. According to the method, the visual sensitive area can be accurately identified, and the code rate is remarkably reduced while the subjective visual quality is guaranteed.

The invention discloses a VVC code rate control algorithm based on deep reinforcement learning, and the algorithm comprises the following steps: importing a video sequence into an encoder, and enabling the video sequence to enter initial frame coding; after the encoder completes the default encoding of the first two frames, the subsequent frame prediction firstly extracts the encoding state information of the previous prediction frame, and a greedy strategy is adopted to perform action selection; an overall reward value is obtained through CTU-level code rate control and an actual coding process in sequence; observing the next state and the last reward, performing TD iteration on the Q value, and adding the Q value into the Q value network after TD iteration; setting an experience playback pool; after the capacity of the experience playback pool reaches a threshold value, randomly sampling from the experience playback pool in batches; resetting the Q value network; and carrying out coding test based on the Q value network obtained by training. The VVC code rate control algorithm based on deep reinforcement learning has a good capability of guiding code rate control coding, and compared with standard code rate control in VTM13.0, the VVC code rate control algorithm based on deep reinforcement learning can bring high rate distortion performance and improve code control precision.

The invention relates to a three-dimensional point cloud prediction geometric coding method based on deep learning, and the method comprises the steps: 1, forming a prediction tree through points collected by each laser transmitter, and converting an original laser radar point cloud LPC into a plurality of prediction trees for representation; 2, respectively designing different predictors and entropy encoders for each component of the coordinates of the points, and compressing each component of the coordinates of the points by applying different quantization step lengths; 3, selecting a quantization step size for quantifying each component by adopting a quantization step size selection strategy; 4, adopting an entropy model to model the probability distribution of the residual error of each component so as to encode the residual error entropy; and step 5, decoding is realized through a reverse process from the step 1 to the step 4. Compared with other methods, the method provided by the invention obtains the best rate distortion performance.

The invention provides a video coding method, which comprises the following steps of: inputting a coding tree unit to be coded, and extracting texture features of a coding block, the texture features comprising local binary pattern similarity of all pixel points in the coding block and direction dispersion of each pixel point; inputting the texture features into the trained multi-type division prediction architecture, and sequentially judging whether to execute quadtree division, horizontal or vertical division, horizontal binary tree or ternary tree division and vertical binary tree or ternary tree division or not through a binary classification problem method; and calculating the rate distortion cost under each division mode to obtain an optimal division mode and a suboptimal division mode of the coding block, and outputting an optimal mode or a combination of the optimal mode and the suboptimal mode. According to the invention, the accuracy of the prediction mode in coding is improved.

Systems and methods directed to a rate-distortion optimization process. One example is directed to a video encoding device including an electronic processor. The electronic processor is configured to obtain motion information associated with a video block and determine, as a function of the motion information, a subset of coding tools from a plurality of coding tools for rate-distortion performance evaluation of the video block.

A better rate distortion ratio is achieved by making interrelationships between coding parameters of different planes available for exploitation for the aim of redundancy reduction despite the additional overhead resulting from the need to signal the inter-plane prediction information to the decoder. In particular, the decision to use inter plane prediction or not may be performed for a plurality of planes individually. Additionally or alternatively, the decision may be done on a block basis considering one secondary plane.

In one implementation, an encoder obtains a first value indicating a number of bits used to encode a current block in a picture, under a coding option, and obtains a second value indicating a distortion between a reconstructed version and an original version of the current block associated with the coding option. The encoder obtains a cost function based on the first value and the second value for the current block. The encoder also obtains another cost function for the current block, associated with a current best coding option. By comparing the cost function and the another cost function, wherein the cost function or the another cost function is scaled by a complexity factor indicating computational complexity associated with the coding option, the encoder updates the another cost function and the current best coding option based on the comparison result.

The invention provides a method and apparatus for loop filtering. The loop filtering method comprises the following steps: acquiring an MCTF (Motion Compensation Time Domain Filtering) filtered image of a current image after MCTF; determining the similarity between the MCTF filtering image and a filtering reference image when the MCTF is carried out on the current image; and determining whether to perform rate distortion optimization of loop filtering using an original image of the current image or the MCTF filtered image based on the similarity.

In various implementations, method and devices are disclosed that involves video encoding based on a Lagrangian rate-distortion cost using a Lagrangian parameter. In a variant, a set of coding modes responsive to a Lagrangian rate-distortion cost comprises an adaptive in-loop filtering mode and wherein determining an adaptive in-loop filtering mode based on a Lagrangian rate-distortion cost using a Lagrangian parameter further comprises modifying a Lagrangian parameter for determining the adaptive in-loop filtering mode.

Different implementations are described, particularly implementations for determining a set of predictor candidates for affine merge coding mode from neighboring blocks for motion compensation of a picture block based on a motion model. The motion model, may be, e.g., an affine model in a merge mode or AMVP mode for a video content encoder or decoder. The motion model, may be, e.g., an affine model based on top-left / top-right control point motion vectors or an affine model based on top-left / bottom-left control point motion vectors. Such affine model may be signaled by a flag. In an embodiment, predictor candidates are sorted in the set based on a criterion such as, e.g., a validity check or a vectors coherence cost. In an embodiment, a predictor candidate is selected from the set based on a motion model for each of the multiple predictor candidates, and may be based on a criterion such as, e.g., a rate distortion cost. The corresponding motion field is determined based on, e.g., one or more corresponding control point motion vectors for the block being encoded or decoded. The corresponding motion field of an embodiment identifies motion vectors used for prediction of sub-blocks of the block being encoded or decoded.

The application discloses a video compression transmission method and related equipment, the method comprises the following steps: obtaining a video sequence to be compressed, taking the first frame and the last frame as conditional frames; inputting the video sequence into the encoder of a pre-trained variational autoencoder to obtain a target latent space representation; processing the target latent space representation through the downsampling module of the compressor to generate an extreme compression representation; transmitting the conditional frames and the extreme compression representation to the receiving end to enable the receiving end to reconstruct the video through the upsampling module of the compressor, the generation model and the decoder of the variational autoencoder to obtain a reconstructed video sequence; the application provides time sequence boundary information through the conditional frames, and in combination with the reconstruction capability of the generation model, can effectively reduce the block effect, blur and high-frequency detail loss commonly seen in traditional methods; the conditional frames and the extreme compression representation significantly reduce the transmission code rate and bandwidth demand, can significantly improve the rate distortion performance, and can be widely applied to the technical field of video compression.

This invention discloses deep learning-based video compression using optimized rate distortion. Systems and methods are provided for enhancing image patches of frames in a video to be compressed. These systems and methods enhance image patches to improve the compression of the video. In at least one embodiment, systems and methods are provided for enhancing video content using a learnable pre-filtering network trained with a joint loss function to improve compression.

The application discloses a kind of pre-processing algorithms for remote sensing image coding.The algorithm is mainly to extract image texture feature parameters, extract image texture feature parameters, according to the texture feature parameter bypasses the prediction mode and the division mode with smaller probability.The algorithm mainly includes image texture feature extraction;Fast rate distortion cost estimation;Intra-frame division prediction;Determine candidate prediction mode and division set.The present application, especially for hardware difficult to realize the H.265 encoding algorithm of remote sensing image.The algorithm saves hardware resources, reduces hardware implementation complexity and difficulty under the premise of trying to guarantee image compression quality, effectively utilizes hardware storage resources, and also reduces the time delay of compression and decoding.

A better rate distortion ratio is achieved by making interrelationships between coding parameters of different planes available for exploitation for the aim of redundancy reduction despite the additional overhead resulting from the need to signal the inter-plane prediction information to the decoder. In particular, the decision to use inter plane prediction or not may be performed for a plurality of planes individually. Additionally or alternatively, the decision may be done on a block basis considering one secondary plane.

Systems and methods for upscaling a chroma format of chroma signals internally (i.e. internal chroma format increase (ICFI)), to improve an inner representation of a reconstructed chroma signal are provided. The chroma format of one or more reconstructed frames may be increased in comparison to one or more original frames. The one or more original frames may be upscaled before coding. In some implementations, one or more residuals may be coded in an original (compressed and / or lower resolution) chroma format (e.g. not upscaled) to avoid increasing an amount of encoded data. In some implementations, the chroma format of the one or more residuals may be increased, but a quantization parameter (QP) for quantizing one or more chroma residuals may be increased to maintain data rates. In some implementations, rate-distortion may be modified at an encoder to account for upscaling the original chroma format.

In various implementations, method and devices are disclosed that involves video encoding based on a Lagrangian rate-distortion cost using a Lagrangian parameter. In a variant, a set of coding modes responsive to a Lagrangian rate-distortion cost comprises an adaptive in-loop filtering mode and wherein determining an adaptive in-loop filtering mode based on a Lagrangian rate-distortion cost using a Lagrangian parameter further comprises modifying a Lagrangian parameter for determining the adaptive in-loop filtering mode.

The embodiment of the application provides a kind of intra prediction method, encoder, decoder and storage medium, comprising: traversing intra prediction mode, determine the initial prediction value of the initial prediction block corresponding to current block.It is respectively carried out intra prediction filtering and intra prediction smoothing filtering processing to initial prediction block, obtain first type prediction value and second type prediction value;Intra prediction smoothing filtering is the process that a plurality of adjacent reference pixels in each adjacent reference pixel set in at least two adjacent reference pixel sets are filtered to current block.Using initial prediction value, first type prediction value and second type prediction value, rate distortion cost calculation is carried out with the original pixel value of current block, determine the current prediction mode corresponding to optimal rate distortion cost.Using current prediction mode, intra prediction is carried out to current block.The index information of current prediction mode and filter identification are written in code stream, and filter identification represents the identification corresponding to intra prediction filtering and / or intra prediction smoothing filtering.

The application provides a rate distortion prediction-based deep video encoder code rate control method and system, which comprises the following steps: step 1, training a prediction module; step 2, inputting a video frame into the prediction module to obtain a prediction point set; step 3, fitting a code rate and quality model according to the prediction point set; step 4, obtaining a frame-level code rate allocation ratio through a code rate control algorithm; and step 5, determining the corresponding encoding parameters of each frame and inputting the encoding parameters into an encoder for encoding. The application directly utilizes a neural network and an original video to predict the code rate model and the quality model of each frame for the first time, without pre-encoding; the video frame is down-sampled to a fixed small resolution before being inputted into the neural network, so that the efficiency is improved and the generalization is enhanced; and the application realizes code rate control at a mini-GOP level for the first time. Compared with the existing code rate control methods, the application can realize the same code rate control accuracy and finer code rate control granularity at a faster speed.

Apparatuses and methods are disclosed for encoding mesh data. Techniques disclosed include receiving a sequence of frames, each of which includes mesh data. For a frame in the sequence, techniques disclosed for encoding the mesh data of the frame according to a static path and according to a motion path of a multipath encoder, computing a static path cost of the encoding according to the static path and a motion path cost of the encoding according to the motion path, where the costs are computed by optimizing a rate-distortion cost function, and selecting, based on the computed motion path cost and static path cost, a bitstream generated by the encoding according to the motion path or a bitstream generated by the encoding according to the static path.

The present application discloses a standing wave angular rate error compensation method, device, equipment and storage medium, relating to the technical field of error compensation, including: obtaining a two-dimensional vibration equation corresponding to a resonator; obtaining a target standing wave angular rate error formula corresponding to the resonator based on the two-dimensional vibration equation, the driving electrode error and the detection electrode error corresponding to the resonator; controlling the resonator to rotate at different target speeds, obtaining the variation relationship of the standing wave angular rate error corresponding to the resonator with each target speed during the rotation process, and compensating the standing wave angular rate according to the target standing wave angular rate error formula and the variation relationship; wherein, the standing wave angular rate error includes the error generated when the resonator rotates under the condition of not less than the target speed. By compensating the error generated when the resonator rotates under the condition of not less than the target speed, the problem of output angle or angular rate distortion is solved.

Methods and apparatus are provided for adaptive quantization parameter selection based on a cost using a neural network. In one embodiment, the neural network can be trained from a training database comprising numerous coding units or blocks. In another embodiment, a rate distortion cost is determined for combinations of quantization parameter and split configuration. The quantization parameter with a minimal rate distortion cost for a particular split is used for encoding. In another embodiment, rate and distortion are separately approximated, resulting in lowest rate cost and lowest distortion cost for each possible type of split.

Techniques are described for storing (302) BC7 texture blocks on disk with lower precision index data and transcoding (304) back to the GPU native BC7 format when loading data. And transcoding back to the standard BC7 is lossless.

Techniques and systems for adaptive bitrate optimization for live content delivery are disclosed. In some embodiments, such techniques may include extracting, in real-time, features from content segments of a video stream; for each content segment of the video stream, identifying a rate-distortion (RD) cluster from a mapping of extracted features to RD clusters using one or more trained machine learning models; and transcoding the video stream by applying, to the content segments, a transcoding ladder having bitrate information corresponding to each identified RD cluster.

A kind of machine vision rate distortion encoding method of license plate recognition task, by obtaining license plate image data set, extracting saliency feature, modulation adaptive quantization parameter, rate distortion optimization and fast division, license plate recognition step composition.The present application extracts background, vehicle, license plate three-level area feature using target detection network, calculates normalized perception intensity, uses hyperbolic tangent function to dynamically adjust the quantization parameter of coding unit, and optimizes the coding division process by combining subsection depth constraint strategy.Solves the technical problems of license plate character target blur and machine recognition rate decline under the conditions of application of video coding on unmanned aerial vehicle and limited code rate, reduces the encoding code rate and computational complexity, preserves the high-frequency microscopic details of license plate characters, improves the recognition accuracy and robustness of machine vision.The present application has the advantages of balancing code rate, low computational complexity, high license plate recognition accuracy, and can be used for unmanned aerial vehicle long-distance license plate recognition.

A method and apparatus for predicting motion vector for inter-frame encoding are provided. An implementation scheme of the method includes: acquiring first and second sets of motion vectors; dividing, in response to the number of valid adjacent PUs being greater than or equal to a preset number, the second set of motion vectors into at least one motion vector subset; calculating a correlation between the first set of motion vectors and each motion vector subset respectively, to obtain a priority of each adjacent PU; calculating, sequentially according to the priority in descending order, a rate distortion based on a motion vector of each adjacent PU, and stop calculating until a rate distortion smaller than a predetermined threshold is obtained; and determining a motion vector of an adjacent PU used when the rate distortion smaller than the predetermined threshold is obtained as the motion vector of the current PU.

The embodiment of the invention discloses a coefficient feature-based division decision method and device, equipment and a storage medium. According to the technical scheme provided by the embodiment of the invention, the skipping judgment threshold is determined according to the size information of the current coding block and the number of the non-zero coefficients by determining the number of the non-zero coefficients in the transformation coefficients of the current coding block and the rate distortion cost of the optimal mode; determining a first non-block division skipping strategy for the current coding block according to the rate distortion cost and a skipping judgment threshold, the first non-block division skipping strategy being used for indicating whether to skip non-block division prediction for the current coding block, and performing division decision on the current coding block based on the first non-block division skipping strategy, the skipping judgment threshold value is flexibly determined according to the number of non-zero coefficients in the transformation coefficients of the current coding block and the rate distortion cost of the optimal mode, so that a good video coding effect can be ensured while the video coding efficiency is improved.