20 results about "Arithmetic encoder" patented technology

The application provides a lightweight variable bit rate multi-view image compression method and system, and relates to the technical field of image compression. The method comprises: downsampling, feature extraction and feature scaling of a single-view image to obtain a latent representation corresponding to a target bit rate; quantization and lossless entropy coding of the latent representation to obtain a final compressed bit stream; subsequent lossless entropy decoding and inverse scaling to restore the latent representation; feature fusion and upsampling of the restored latent representations of different views to generate a reconstructed compressed image. The model comprises a main encoder, a feature scaling module, a quantization module, an autoregressive entropy model, an arithmetic encoder, an arithmetic decoder, a feature inverse scaling module and a decoder. The application can efficiently compress image data, reduce computational complexity and storage space occupation while preserving image details and quality, thereby providing faster speed and lower bandwidth requirement for image transmission and storage.

Certain aspects of the present disclosure provide a method for wireless communications by a transmitter. The method generally includes obtaining a first block of information bits from a buffer, generating a first sequence of probabilistic amplitude shaped (PAS) symbols, from the first block of information bits, using a demapper function and an arithmetic decoder function, transmitting the first sequence of PAS symbols to a receiver, combining a first subset of the first block of information bits, identified by an arithmetic encoder function and a mapper function, with a second block of information bits from the buffer, generating a second sequence of PAS symbols, from the combined first subset of the first block of information bits and second block of information bits, using the demapper function and the arithmetic decoder function, and transmitting the second sequence of PAS symbols to the receiver.

The invention describes an encoding scheme for arithmetically encoding a sequence of information values into an arithmetic coded bitstream using providing the bitstream with entry point information allowing for resuming arithmetic decoding the bitstream from a predetermined entry point onward. A respective decoding scheme is also provided. These encoding and decoding schemes provide more efficient encoding concept in view of the decoding speed.

An arithmetic encoder for encoding a plurality of symbols having symbol values to derive an interval size information for an arithmetic encoding of one or more symbol values to be encoded based on a plurality of state variable values representing statistics of a plurality of previously encoded symbol values with different adaptation time constants. The arithmetic encoder maps a first state variable value, or a scaled and / or rounded version thereof, using a lookup-table and maps a second state variable value, or a scaled and / or rounded version thereof using the lookup-table, to obtain the interval size information describing an interval size for the arithmetic encoding of one or more symbols to be encoded. Further arithmetic encoders, arithmetic decoders, video encoders, video decoder, methods for encoding, methods for decoding and computer programs are also disclosed.

Embodiments comprise an arithmetic encoder for encoding a plurality of symbols having symbol values, wherein the arithmetic encoder is configured to derive an interval size information for an arithmetic encoding of one or more symbol values to be encoded on the basis of one or more state variable values, which represent statistics of a plurality of previously encoded symbol values, wherein the arithmetic encoder is configured to update the one or more state variable values in dependence on an encoded symbol, in order to obtain one or more updated sate variable values for an encoding of a subsequent symbol; wherein the arithmetic encoder is configured to use a first, comparatively faster adaptation rate for an update of one or more of the state variable values for a predetermined number of adaptation steps; and wherein the arithmetic encoder is configured to use a second, comparatively slower adaptation rate for the update of one or more of the state variable values when the predetermined number of adaptation steps is completed. Furthermore, an arithmetic encoder is disclosed which is configured to determine one or more initialization values for the one or more state variable values in dependence on a quantization parameter which determines a quantization step size for a quantization of one or more values to be encoded using the symbols. Furthermore, corresponding decoders, methods, computer programs, encoded representations and data streams are disclosed.

This invention discloses a context-based adaptive entropy coding method for audio, comprising the following steps: First, the quantized codebook index sequence Q and quantization side information S of the audio signal are sequentially fed into a causal convolutional context model, a cross-band attention network, and a cascaded codebook dependency network for processing, to obtain temporal context feature vectors, frequency context feature vectors, and quantization-level context feature vectors for the audio signal in the time domain, respectively. Second, the temporal context feature vectors, frequency context feature vectors, and quantization-level feature vectors are input into a neural probability prediction model for processing, estimating the conditional probability distribution of each codebook index. Third, the predicted conditional probability distributions are input into an adaptive arithmetic encoder to generate the encoded bitstream, and necessary header information is added to the encoded bitstream to form a complete bitstream. This invention can further improve the compression efficiency of audio coding.

An encoding scheme is provided for arithmetically encoding a sequence of information values into an arithmetic coded bitstream by providing the bitstream with entry point information, allowing for resuming arithmetic decoding of the bitstream from a predetermined entry point onward. A respective decoding scheme is also provided. These encoding and decoding schemes provide more efficient encoding in view of the decoding speed.

An example method of video coding includes receiving a video bitstream comprising coding information for a plurality of blocks. The method also includes obtaining, from the video bitstream, respective values for a set of parameters associated with a block in the plurality of blocks, the set of parameters corresponding to an arithmetic coder probability update rate. The set of parameters comprises A, B, C, and E parameters, and the arithmetic coder probability update rate is determined according to an inverse of 2 raised to the power of (A+(count>B)+(count>C)+g(M, E)). The method further includes determining a coding context based on the arithmetic coder probability update rate, and decoding the block in the plurality of blocks based on the coding context.

An arithmetic encoder for encoding a plurality of symbols having symbol values is configured to derive an interval size information for an arithmetic encoding of one or more symbol values to be encoded on the basis of a plurality of state variable values, which represent statistics of a plurality of previously encoded symbol values with different adaptation time constants. The arithmetic encoder is configured to map a first state variable value, or a scaled and / or rounded version thereof, using a lookup-table and to map a second state variable value, or a scaled and / or rounded version thereof using the lookup-table, in order to obtain the interval size information describing an interval size for the arithmetic encoding of one or more symbols to be encoded. Further arithmetic encoders, arithmetic decoders, video encoders, video decoder, methods for encoding, methods for decoding and computer programs are also disclosed which are based on the same concept and on other concepts.

The present embodiment relates to a method and technical equipment for implementing a method. The method comprises: i) a step of determining a first coding tree unit; ii) a step of determining a coding unit belonging to the first coding tree unit; iii) a step of determining a bin used by an arithmetic encoder / decoder to represent a syntactic element of the coding unit; iv) a step of determining whether the bin performs a position-based context probability update for a second coding tree unit—the determination includes determining the context class of the syntactic element, determining whether the context class belongs to a set of context classes to be used for a probability update for the second coding tree unit, and determining whether the number of bins having the same context class as the bin's context class is less than a selected maximum number—and v) a step of using the bin to update a probability state parameter used in the second coding tree unit according to the result of step iv).

An encoding scheme is provided for arithmetically encoding a sequence of information values into an arithmetic coded bitstream by providing the bitstream with entry point information, allowing for resuming arithmetic decoding of the bitstream from a predetermined entry point onward. A respective decoding scheme is also provided. These encoding and decoding schemes provide more efficient encoding in view of the decoding speed.

The application discloses an end-to-end image lossless compression method based on two-dimensional non-separable wavelet transform. The method decomposes an image through multi-stage wavelet transform to obtain sub-band coefficients of different sizes. The coefficients are input into an entropy coding model, and an arithmetic encoder driven by a context model is used to compress the coefficients into a bit stream. The application adopts two-dimensional non-separable wavelet transform, uses a lifting structure of wavelet transform, contains three prediction filters and an update filter, and the filter structure is a convolutional neural network structure. The entropy coding module also uses a neural network structure. All parameters are optimized as a whole through deep learning, the network is trained until convergence, and the trained model is used to realize image lossless compression. The application has excellent performance in lossless compression, is significantly improved compared with a traditional JPEG2000 lossless compression method, and is also superior to an end-to-end image lossless compression method based on a traditional separable wavelet transform.

This application provides a geometric-aware compression and reconstruction method and system for 360-degree panoramic images. The method includes generating a position code based on the latitude and longitude coordinates of the equidistant cylindrical projection of the 360-degree panoramic image, serving as geometric prior information; using the position code to drive a geometrically aware deformable convolutional network in the encoding end to perform geometrically adaptive feature extraction, obtaining coded features; in a hierarchical prior network, fusing the position code and the coded features to perform spatially adaptive probability distribution estimation, and generating a binary bitstream through an arithmetic encoder; decoding the binary bitstream through an arithmetic decoder to recover latent space features; and at the decoding and reconstruction end, based on the position code, performing multi-stage correction on the latent space features through a reconstruction-aware spatial attention module to restore high-frequency details and output a reconstructed image. This application can achieve the goal of improving the compression efficiency and reconstruction quality of panoramic images.

An encoding scheme is provided for arithmetically encoding a sequence of information values into an arithmetic coded bitstream by providing the bitstream with entry point information, allowing for resuming arithmetic decoding of the bitstream from a predetermined entry point onward. A respective decoding scheme is also provided. These encoding and decoding schemes provide more efficient encoding in view of the decoding speed.

An arithmetic encoder for encoding a plurality of symbols having symbol values is configured to derive interval size information for arithmetic encoding of one or more symbol values to be encoded based on a plurality of state variable values, the plurality of state variable values represent statistics of a plurality of previously encoded symbol values having different adaptation time constants. The arithmetic encoder is configured to map a first state variable value or a scaled and / or rounded version thereof using a lookup table, and to map a second state variable value or a scaled and / or rounded version thereof using the lookup table, in order to obtain the interval size information describing the interval size for arithmetic coding of one or more symbols to be coded. Additional arithmetic encoders, arithmetic decoders, video encoders, video decoders, encoding methods, decoding methods, and computer readable media based on the same concept and other concepts are also disclosed.

This invention discloses a deep learning-based intra-frame lossless compression method for video, specifically relating to the field of image compression technology. First, a multi-scale anomaly response map is constructed to characterize weakly predictable regions in an image. Then, a compression difficulty score matrix is ​​generated based on the response map, identifying high-complexity regions and inputting them into a weakly predictable region modeling network, outputting a high-order residual probability field. Further, the residual probability distribution guides an arithmetic encoder to perform adaptive intra-frame lossless compression on the original image, generating a compressed bitstream. The decoder, combined with the shared residual probability field, can losslessly restore the original image pixel by pixel. This invention effectively improves the compression efficiency and restoration accuracy of edge detail regions, and is suitable for video image compression tasks in scenarios such as remote sensing, security, and satellite communication.

The present disclosure relates to methods, encoders, and decoders for processing an image in the presence of a skipping algorithm. This algorithm allows the encoder and decoder to skip the encoding and decoding of insignificant tensor elements based on available standard deviation information. Since elements are not encoded and decoded due to the skipping algorithm, the minimum standard deviation value should be adjusted to the skipping threshold when designing the arithmetic encoder. This reduces the size of tables in the arithmetic encoder and improves coding efficiency.

The invention discloses a video intra-frame lossless compression method based on deep learning, and particularly relates to the technical field of image compression. The method comprises the following steps: firstly, constructing a multi-scale abnormal response graph for depicting a weak predictable area in an image; generating a compression difficulty scoring matrix based on the response diagram, identifying a high-complexity region, inputting the high-complexity region into a weak prediction region modeling network, and outputting a high-order residual probability field; further, an arithmetic encoder is guided by using residual probability distribution to carry out adaptive intra-frame lossless compression on the original image, and a compressed code stream is generated; the decoding end is combined with the shared residual probability field to realize pixel-by-pixel lossless restoration of the original image; the method can effectively improve the compression efficiency and recovery precision of the edge detail area, and is suitable for video image compression tasks in scenes such as remote sensing, security and satellite communication.

The application discloses an image compression method, system and storage medium based on continuous variable code rate allocation, and the method comprises the following steps: acquiring an image to be compressed, inputting the image into a trained image continuous variable code rate image compression network, and outputting an image compression result; the image to be compressed is encoded and decoded, and the encoding end comprises the following steps: extracting an image feature value y, using a joint quantization module to perform joint quantization on the channel dimension and the spatial dimension of the image feature value y, and obtaining a quantized feature value; using an arithmetic encoder to encode the quantized feature value into bit stream data; the decoding end comprises the following steps: after the received bit stream data is decoded by an arithmetic decoder, joint dequantization is performed on the channel dimension and the spatial dimension, and a decoded image feature value y is obtained ’ ; the decoded image feature value y ’ is subjected to feature reconstruction, and a reconstructed image is obtained. The application can dynamically control the bit rate during image compression, reduce the bit error rate, and improve the quality of the compressed image.

An encoding scheme is provided for arithmetically encoding a sequence of information values into an arithmetic coded bitstream by providing the bitstream with entry point information, allowing for resuming arithmetic decoding of the bitstream from a predetermined entry point onward. A respective decoding scheme is also provided. These encoding and decoding schemes provide more efficient encoding in view of the decoding speed.