19results about How to "Reduce bit rate" patented technology

The invention relates to an underground video coding method and device, and the method comprises the steps: obtaining to-be-coded video data based on the perception capability evaluation and video reconstruction of an underground video stream; obtaining a time-space domain saliency model based on the human eye visual perception structure; and carrying out bit allocation and code rate allocation on the to-be-coded video data based on the time-space domain saliency model, and determining coded data corresponding to the underground video stream. The underground video is subjected to lossless reconstruction based on a human visual perception mechanism, so that the visual redundancy is removed, the code rate is reduced, and the underground bandwidth is relieved; bit allocation and code rate allocation are performed on the to-be-coded video data through the time-space domain saliency model, so that the coding efficiency and the visual experience degree are remarkably improved, and the method has wide application value.

The embodiment of the invention provides a video coding quantization parameter offset determination method, device and equipment, and the method comprises the steps: obtaining a to-be-processed video frame, carrying out the adaptive quantization processing of the to-be-processed video frame, and obtaining the reference quantization parameter offset of each coding block in the to-be-processed video frame; determining a reference intervention value based on a reference quantization parameter offset of the coded block; in the process of performing block-level quantization processing on each coding block, under the condition that the current coding block meets the condition that the reference quantization parameter offset is greater than the reference intervention value, reducing the intensity factor corresponding to the current coding block; and determining a target quantization parameter offset of the current coding block based on the down-regulated intensity factor, a set function mapping relation and preset information of the current coding block, and using the target quantization parameter offset to perform video coding of the to-be-processed video frame. According to the scheme, under the condition that the subjective experience is not obviously reduced, the code rate is saved, or the subjective image quality is improved under the same code rate.

The invention discloses an aurora spectral image lossless compression method based on time-space spectrum combination of Transform. A neural network based on the Transform can improve the modeling precision and the prediction accuracy; time correlation is introduced, and meanwhile, reference pixels in time, space and spectrum directions are used for prediction, so that the prediction accuracy can be improved, and redundancy can be further eliminated; and by introducing exception processing, the code rate can be further reduced.

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 present application discloses a joint stereo audio coding and decoding method and device, belonging to the field of audio coding and decoding technology. The method mainly includes: encoding the Bluetooth audio of the left and right channels to the time domain noise shaping module according to the standard Bluetooth coding steps, respectively, to obtain the left channel spectral coefficient and the right channel spectral coefficient corresponding to the left and right channels respectively; jointly encoding the left channel spectral coefficient and the right channel spectral coefficient, to obtain the left channel joint coding data corresponding to the left channel and the right channel joint coding data corresponding to the right channel; and performing the remaining coding steps on the left channel joint coding data and the right channel joint coding data according to the standard Bluetooth coding steps, respectively, to obtain the code streams corresponding to the left channel and the right channel respectively. Under the premise of ensuring the sound quality, the present application makes full use of the correlation between the stereo dual channels, improves the efficiency of stereo coding, greatly reduces the bit rate, saves the Bluetooth air bandwidth, thereby reducing interference and improving the user experience.

The invention discloses an attribute information decoding and coding method, device and equipment, and belongs to the technical field of communication, and the decoding method comprises the steps: carrying out the decoding and inverse quantization of to-be-decoded data, and obtaining a reconstruction transformation coefficient corresponding to each node; the reconstruction transformation coefficients comprise a first reconstruction transformation coefficient corresponding to the brightness component, a second reconstruction residual transformation coefficient corresponding to the first chrominance component and a third reconstruction residual transformation coefficient corresponding to the second chrominance component; determining a second reconstruction transformation coefficient according to the first reconstruction transformation coefficient and the second reconstruction residual transformation coefficient; executing at least one of the following steps: determining a third reconstruction transformation coefficient corresponding to the second chrominance component according to the second reconstruction transformation coefficient and a third reconstruction residual transformation coefficient; determining a third reconstruction transformation coefficient according to the first reconstruction transformation coefficient and the third reconstruction residual transformation coefficient; and determining a third reconstruction transformation coefficient according to the first reconstruction transformation coefficient, the second reconstruction transformation coefficient and the third reconstruction residual transformation coefficient.

The invention provides a video coding method and device and a video decoding method and device, and relates to the technical field of video coding and decoding. Comprises: obtaining a student model; the student model is a model obtained by guiding a first network model to train through a teacher model, the teacher model is a model obtained by training a second network model, and when the same video is coded based on the first network model and the second network model respectively, the first network model and the second network model are selected; the code rate of the coding result of the first network model is smaller than that of the coding result of the second network model; and obtaining a coding result of the video to be coded according to the student model. According to some embodiments of the invention, in a video coding scheme for carrying out video coding by using a deep learning network model, the video reconstruction quality is ensured, and the code rate of a video coding result is reduced at the same time.

This application provides a decoding and encoding method, apparatus, and device. The method includes: obtaining a target probability distribution model corresponding to a first scaling factor based on a first bitstream corresponding to a current block; decoding a second bitstream corresponding to the current block based on the target probability distribution model corresponding to the first scaling factor to obtain corrected residual features corresponding to the current block; and determining a reconstructed image block corresponding to the current block based on the corrected residual features. This application improves encoding and decoding performance.

The invention relates to a video compression model processing method, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that a to-be-trained student model and a trained teacher model are acquired, the teacher model and the student model are video compression models based on a neural network, the teacher model is configured to realize video compression of a first code rate, the student model is configured to realize video compression of a second code rate, and the first code rate is greater than the second code rate; video compression is carried out on the sample video through the student model and the trained teacher model, training losses are constructed based on compression results obtained in respective compression processes, and the training losses comprise distillation losses between the student model and the trained teacher model and reconstruction losses of the student model; and performing parameter adjustment on the student model according to the training loss until the model converges to obtain a trained student model. By adopting the method, it can be ensured that the compressed video has low code rate and high video quality at the same time.

The deep learning-based video multi-level adaptive compression cross-layer transmission optimization method and system of the application relates to the technical field of video transmission. By designing a compression level selection model based on deep learning, the optimal compression level is output based on the original video data and network environment parameters, and the corresponding compression strategy of the optimal compression level is obtained. A transmission strategy selection model is designed to generate a transmission strategy based on video quality parameters and network environment parameters. A global utility function is defined to calculate the global utility value when the compression strategy and the transmission strategy are assumed to be executed. An optimization objective function is designed, which inputs the compression strategy and the transmission strategy and outputs the optimized compression strategy and the transmission strategy. A condition judgment logic is set to select the final strategy based on the compression strategy and the transmission strategy before and after optimization, thereby achieving global optimization of video compression and transmission.

This application discloses an HDR video coding method, apparatus, device, and medium based on texture-luminance perceptual masking, relating to the field of video coding. The method includes: converting the linear luminance average value of the area to be encoded to the target luminance domain to obtain a first logarithmic value; determining a luminance masking component based on the first logarithmic value; performing a frequency domain transformation on the prediction residual of the area to be encoded to obtain frequency domain coefficients, accumulating the energy of the frequency domain coefficients to obtain an energy value, and converting the energy value into a texture masking component; mapping the luminance masking component and the texture masking component to the same numerical interval, and fusing the two components after unifying the interval to obtain a perceptual factor; adjusting the original Lagrange multipliers based on the perceptual factor, constructing a rate-distortion cost function using the adjusted Lagrange multipliers, and performing video coding processing on the area to be encoded according to the rate-distortion cost function to obtain the encoded HDR video. This application significantly improves coding quality and efficiency.

The present application provides a method and device for video classification, the method comprising: receiving a video from a video source; obtaining M video frames P from the video; 1 , P 2 ,…,P M and M adjacent frames Q 1 , Q 2 , …, Q M , where the adjacent frame Q 1 is the video frame P 1 The adjacent video frames, the adjacent frames Q 2 is the video frame P 2 adjacent video frames, ..., the adjacent frames Q M is the video frame P M Adjacent video frames; Determine the distance D of M videos 1 , D 2 , …, D M , where the video distance D 1 is the video frame P 1 and adjacent frame Q 1 The Hamming distance between them, video distance D 2 is the video frame P 2 and adjacent frame Q 2 The Hamming distance between, ..., video distance D M is the video frame P M and adjacent frame Q M The Hamming distance between the M videos is D 1 , D 2 , …, D M are compared with the threshold values ​​to obtain the comparison result {H 1 , H 2 , ..., H M}; According to the comparison result {H 1 , H 2 , ..., H M}Determine the classification result to which the video belongs; compress the video according to the classification result and send it to the content distribution system.

The invention provides a three-dimensional point cloud coding method and device, a three-dimensional point cloud decoding method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The coding method comprises the following steps: down-sampling an original point cloud according to a down-sampling parameter to obtain a down-sampling point cloud and target information of each point in the down-sampling point cloud, the down-sampling parameter being a non-integer greater than 1; determining neighbor occupation information of each point in the down-sampling point cloud; clustering the down-sampling point clouds according to the down-sampling parameters to obtain a point cloud cluster; training an interpolation network according to the clustering clusters according to the target information and the neighbor occupation information of each point in each point cloud clustering cluster to obtain optimized interpolation network parameters of each clustering cluster; and performing lossless coding on the down-sampling point cloud to obtain down-sampling point cloud coding data, and coding the interpolation network parameters of each cluster to obtain network coding data, so that point cloud coding of fractional down-sampling can be effectively supported, and more flexible and efficient point cloud compression can be realized.

The present invention discloses an image compression method based on a deep self-attention transformation network. The specific steps of the method are as follows: S1, collect and organize standardized high-definition image data sets, construct neural network training sets, test sets and cross-validation sets; S2, establish a multi-layer neural network based on deep self-attention transformation, the neural network includes a coding network and a decoding network, and a deep self-attention transformation module is added to both the coding network and the decoding network for feature information aggregation; train the coding network and the decoding network; S3, input the image into the coding network to obtain the output data, and obtain a compressed code stream after quantization and entropy coding; S4, reconstruct and restore the compressed image through a decoding network symmetrical to the coding network. The image compression method of the present invention can save an average of about 20% of the code rate at the same quality in an equal comparison with the traditional image compression method BPG on a large number of test images.

The present application discloses a method and apparatus in a user equipment and a base station used for wireless communication. The user equipment receives a first wireless signal and a second wireless signal, and then sends K first-class bit sequences in K time-frequency resources respectively. The first bit block is used to indicate whether the first wireless signal is correctly received, and the first bit block includes a positive integer number of bits; the second bit block includes feedback for the second wireless signal, and the second bit block includes a positive integer number of bits; each of the K first-class bit sequences includes a positive integer number of bits; the first bit block and the second bit block are used to generate each of the K first-class bit sequences; K is a positive integer. The above method improves the transmission reliability of control information.

The invention provides a video coding and decoding method and device, electronic equipment and a computer readable storage medium, and the method can improve the coding and decoding efficiency, and comprises the steps: obtaining a target quantization coefficient in a target region in a current block; determining a sub-block transformation mode corresponding to the current block according to a target quantization coefficient in the target region; and decoding the current block according to the sub-block transformation mode corresponding to the current block.

The present application discloses a video encoding and decoding method, a video codec and an electronic device, and belongs to the field of electronic technology. The video encoding and decoding method comprises: obtaining a first video frame sequence to be encoded; inputting the first video frame sequence into a time-series reversible neural network to obtain a video encoding code stream corresponding to a first preset distribution outputted in a forward direction by the time-series reversible neural network; in the case of decoding, inputting the video encoding code stream and a second preset distribution into the time-series reversible neural network to obtain a second video frame sequence outputted in a reverse direction by the time-series reversible neural network.

The invention relates to the technical field of satellite remote sensing images, in particular to a remote sensing image ultra-low code rate compression method and device based on vector diagram assistance, and the method comprises the steps: obtaining a remote sensing image set and a vector diagram of each remote sensing image in the remote sensing image set, so as to construct a training set and a test set; performing optimization training on a pre-constructed remote sensing image ultra-low bit rate compression network based on vector diagram assistance by using the training set, so as to obtain a trained remote sensing image ultra-low bit rate compression network based on vector diagram assistance, the vector diagram assistance-based remote sensing image ultra-low code rate compression network comprises a hidden space compression module and a conditional diffusion model; and inputting the test set into the trained vector diagram assistance-based remote sensing image extremely low bit rate compression network so as to reconstruct a compressed remote sensing image. Therefore, the problem that an image with high perceptual quality and accurate semantic information cannot be reconstructed at an extremely low bit rate by a traditional codec and a method based on deep learning is solved.

The embodiment of the application discloses a coefficient coding and decoding method, an encoder, a decoder and a computer storage medium, which are applied to the decoder, and the method comprises the following steps: based on the position of a to-be-decoded quantization coefficient, a first context model adaptive binary arithmetic coding mode is used to analyze a code stream to obtain the value of non-zero identification information; in the case that the value of the non-zero identification information is a first value, a second context model adaptive binary arithmetic coding mode is used to analyze the code stream to obtain the value of X preset identification information; in the case that the value of the Xth preset identification information is the first value, the target order of the exponential golomb code is determined, and the code stream is analyzed by using the exponential golomb decoding algorithm of the target order to obtain the residual value of the reconstructed quantization coefficient absolute value; and the value of the non-zero identification information, the value of the X preset identification information and the residual value of the reconstructed quantization coefficient absolute value are used to determine the reconstructed quantization coefficient absolute value corresponding to the quantization coefficient position.