243 results about "Discrete cosine transforms" patented technology

The invention relates to an intelligent visual identification technology-based cold solder joint microcrack intelligent discrimination method, which comprises the steps of collecting an initial RGB image of a to-be-detected welding spot, carrying out two-dimensional discrete cosine transform and inverse two-dimensional discrete cosine transform on the initial RGB image to obtain an enhanced image, and fusing the enhanced image with an R channel of the initial RGB image to obtain a fused image; forming a dual-channel feature map; calculating the phase consistency of the dual-channel feature map, and obtaining a suspected candidate region of the pseudo soldering microcrack through an adaptive threshold segmentation method; acquiring an RGB image sequence of a continuous time sequence of the welding spots, and performing anomaly detection to obtain an abnormal region set; and constructing a welding spot thermal diffusion model, and inputting the geometric parameters and the environmental parameters in the abnormal region set into the welding spot thermal diffusion model to obtain a final judgment result of the pseudo soldering microcracks. According to the method, through multi-dimensional feature fusion and continuous time sequence dynamic tracking, the detection precision of the pseudo soldering microcracks is remarkably improved, the false detection rate is reduced, and the final judgment result is more accurate.

The invention provides a deep counterfeit multi-label sorting and positioning method based on multi-domain feature fusion. The deep counterfeit multi-label sorting and positioning method comprises the following steps: preprocessing a sequence deep counterfeit data set; inputting the human face RGB image into a DINOv2-based spatial feature extractor to carry out spatial domain feature extraction; a face RGB image is decomposed through discrete cosine transform to obtain a plurality of frequency domain coefficients and frequency spectrums, the obtained frequency spectrums are flattened and remodeled, components with the same frequency are grouped into a channel to form new input, and then the original color input of the face RGB image is finally converted into frequency domain feature representation through an encoder. The spatial domain features and the frequency domain features are utilized, splicing fusion operation is carried out through a cross attention module, new multi-domain representation of the face deep counterfeit image with the spatial domain and the frequency domain is formed, and then the multi-domain representation is input into a graph attention network to capture the complex relation between different features. According to the method, a more accurate deep counterfeiting analysis result can be realized.

The invention relates to the technical field of digital image security and authentic identification, and provides an image tampering detection method and system based on mixed features and RGB features, and the method comprises the steps: obtaining a to-be-detected input image, and carrying out the preprocessing of the to-be-detected input image; respectively extracting a Haar wavelet high-frequency component, a discrete cosine transform frequency domain feature and a Bayer convolution noise feature, and carrying out matrix level fusion to obtain a mixed feature; extracting RGB (Red, Green and Blue) features for the preprocessed input image; the mixed features are connected through cross-layer residual errors, and mixed feature learning features are obtained; and integrating the mixed feature learning features and the fused RGB features by using a cross-modal feature interaction architecture to obtain a prediction probability graph. Multi-modal features are fused, high-frequency response is enhanced, and the accuracy of image tampering detection is improved by adopting a dynamic fusion mechanism. The technical problems that an existing tampering detection method is insufficient in feature characterization capacity in a complex scene, low in tampering trace detection sensitivity and the like are solved.

A system and method for compressing synthetic aperture radar (SAR) images with enhanced phase recovery and unwrapping capabilities is disclosed. The system performs preprocessing on input SAR images, applies discrete cosine transform (DCT) to create subbands, and utilizes a multi-pass amplitude compression technique. A specialized neural network performs phase unwrapping using compressed amplitude information and interferogram wrapped phase data. The system employs a channel-wise transformer fusion block (CTFB) for feature fusion and a multi-stage context recovery subsystem with optimized loss functions for both amplitude and phase recovery. The method achieves improved compression efficiency and phase recovery accuracy, particularly beneficial for Interferometric SAR (InSAR) applications.

The invention provides a hydrological time series prediction network and method based on feature extraction and guidance, and belongs to the technical field of hydrological time prediction. The problems of insufficient prediction precision, fast error speed increase and the like of the existing method are solved; an encoder-decoder model structure of Informer is adopted, the system further comprises a feature extraction module, and the output of the feature extraction module serves as the input of the encoder; the feature extraction module comprises a time domain feature extraction module, a frequency domain feature extraction module and a dynamic feature fusion module, and the time domain feature extraction module is used for extracting multi-scale time domain features in the hydrological time sequence data; the frequency domain feature extraction module extracts global frequency domain features in the hydrological time sequence data through discrete cosine transform; the dynamic feature fusion module is used for performing channel-level and space-level fusion on the time domain features and the frequency domain features; an attention guiding mechanism is adopted to replace a sparse attention mechanism in an original encoder; the method is applied to hydrological time series prediction.

The invention relates to the technical field of offshore wind power prediction, and discloses a prediction method fusing a time sequence convolutional network and a Transform model. In order to solve the problems that time sequence frequency features are difficult to fully utilize and prediction precision is limited in the prior art, a discrete cosine transform technology is introduced to carry out frequency domain feature extraction on an original time sequence, a periodic rule is deeply mined, and feature weight distribution is optimized; on this basis, a TCN-Transformer composite model is constructed, the TCN model captures a long-time dependency relationship by using expansion causal convolution, and a processed feature information matrix and a feature information matrix passing through an encoder embedding layer are fused to realize multi-dimensional information complementation; and the Transform module is used for carrying out deep analysis and dynamic prediction on the fused features through a self-attention mechanism. According to the method, the offshore wind power prediction precision is remarkably improved, the method can be applied to the field of power grid dispatching and energy optimization, and reliable support is provided for efficient utilization of offshore wind power.

The invention discloses an embedded image decoding display system based on an ARM, and relates to the technical field of computers, and the system comprises a dynamic sensing module which is used for monitoring the CPU occupancy rate, the cache hit rate and the memory bandwidth utilization rate of an ARM processor in real time, establishing a three-dimensional resource vector containing a dynamic weight coefficient, and calculating a threshold boundary; the adaptive decoding engine comprises an optimization unit which is used for reconstructing a bit stream processing channel and a register data exchange mechanism based on an SIMD expansion instruction of an ARMv8.2 instruction set; the prediction unit is used for predicting instruction-level hotspot distribution of the decoding task by adopting a TinyLSTM model and generating an instruction transmitting strategy; the hardware optimization module is used for executing a heterogeneous collaboration strategy of the NEON coprocessor and the MaliGPU, and the heterogeneous collaboration strategy comprises the following steps: dividing DCT / IDCT (discrete cosine transform / inverse discrete cosine transform) calculation granularity; determining a mixing precision conversion assembly line of the GPU shader according to the color gamut of the display equipment; and the display driving module is used for implementing a decoding parameter dynamic confusion algorithm in the security domain and forming a tamper-proof closed loop with the optimization unit.

The invention discloses a time-frequency double-domain isolation time sequence anomaly detection method based on Mamba-self-attention, which can be applied to the fields of industrial manufacturing, medical equipment and the like and can detect anomaly by quantifying time-frequency difference. The method comprises the steps of extracting a multivariable time sequence sample from business data, and obtaining multivariable data through reversible instance normalization; the input time domain representation module is used for independently inputting a Mama network according to a natural time sequence and an inversion time sequence, capturing forward and reverse time features and fusing the forward and reverse time features into time features; a frequency domain representation module is input, seasonal variables are extracted, frequency features are extracted in combination with discrete cosine transform and an attention mechanism, and the frequency features are reconstructed to a time domain through inverse discrete cosine; the time and frequency characteristics are input into a time-frequency difference module, and the inconsistency is quantified through Kullback-Leible (KL) divergence so as to compare and learn a similarity loss function training model; and generating an anomaly score and setting a hyper-parameter to judge anomaly. The method is based on a bidirectional Mama and self-attention time-frequency double-domain isolation architecture, mode specificity discrimination features masked by traditional fusion are reserved, the consistency of normal mode domains is high, the correlation of abnormal performance is collapsed, and the detection accuracy and reliability are improved.

The invention discloses an electronic document tracing method and device based on dynamic encryption and multi-modal watermarking, and relates to the technical field of information security and digital rights management. The method comprises the following steps: acquiring a PDF document uploaded by a user, encrypting the PDF document by adopting a randomly generated main encryption key, and acquiring and storing the encrypted PDF document; the content of the encrypted PDF document is analyzed, secret information is embedded into a picture in the document based on a two-dimensional discrete cosine transform algorithm, and picture watermark embedding is completed; a self-adaptive watermark embedding algorithm is adopted to embed secret information into a text in the document, and text watermark embedding is completed; related information of the checking operation is written into a block chain database; dynamically extracting watermark information from the divulged PDF document by adopting a watermark extraction algorithm; and querying a traceability database according to the extracted watermark information, and outputting a traceability result by comparing the extracted watermark information with information in a database storing various watermark information. According to the invention, the survival rate and the concealment of the watermark can be improved.

According to the invention, the multi-mode time-frequency domain conversion and the deep learning technology are combined, and a multi-component mixed Raman spectrum unmixing method is developed, so that clinical in-vivo and in-situ detection and disease diagnosis of novel Raman probes, instruments and the like are facilitated. The method comprises the following steps: (1) converting a mixed Raman spectrum from a time domain to a frequency domain by using fast Fourier transform (FFT), discrete cosine transform (DCT) and discrete sine transform (DST), and extracting frequency domain features; (2) extracting multi-scale local time-frequency domain characteristics of the mixed spectrum by using short-time Fourier transform (STFT) and discrete wavelet transform (DWT); (3) carrying out spectral unmixing calculation in each mode in combination with a one-dimensional attention mechanism U-shaped neural network model; and (4) fusing various modal unmixing results by using a meta-learning method, and analyzing the weight of each modal to obtain an accurate unmixing spectrum. Compared with a traditional Raman spectrum analysis method, the Raman spectrum multi-component signal unmixing method based on multi-modal time-frequency domain transformation and deep learning can accurately separate independent Raman signals of different tissue structures and biochemical components in a complex environment in a living body, so that the unmixing accuracy of the Raman spectrum multi-component signal is improved. Therefore, convenience is provided for subsequent disease mechanism analysis and diagnosis. The method provides an innovative and potential solution for in-vivo and in-situ detection analysis and disease diagnosis of medical clinical Raman spectroscopy.

The invention relates to the technical field of man-machine interaction, and provides a robot mechanical arm collision avoidance planning method and system, a medium and equipment, and the method comprises the steps: obtaining posture features through the processing of a feature coding layer and an encoder for the observed posture features of a human body, and carrying out the inverse discrete cosine transform after the posture features are processed through a decoder and a feature decoding layer, obtaining human body posture features at future moments; for observed human body track features, track features are obtained through processing of a feature encoding layer and an encoder, feature fusion is carried out on the track features and posture features through a multi-head attention layer, after the fused track features are processed through a decoder and a feature decoding layer, inverse discrete cosine transform is carried out, and human body track features at the future moment are obtained; calculating the human body posture features at the future moment into a human body model, and performing point cloudization on the human body model to obtain predicted dynamic human body point cloud; and obtaining an object point cloud, taking the object point cloud and the dynamic human body point cloud as an obstacle, and performing collision avoidance trajectory planning.

The invention relates to the technical field of data processing, in particular to a BIM-based traffic engineering construction progress management method and system, and the method comprises the steps: equally dividing a to-be-analyzed asphalt pavement region into a plurality of block pavement regions; the compaction degree of each block pavement area is obtained; constructing a compactness matrix according to the compactness of all block pavement areas in the to-be-analyzed asphalt pavement area; obtaining a plurality of final similar roadbed matrixes; obtaining the similarity of the compactness in the final similar roadbed matrix; determining a final block of the final similar roadbed matrix according to the similarity; and according to the sizes of the final blocks of the final similar roadbed matrixes, discrete cosine transform compression storage is carried out on all the final similar roadbed matrixes. According to the method, the compaction degree data compression and storage effect of the asphalt pavement area in the BIM-based traffic engineering construction progress process is improved.

The invention provides a high-fidelity anti-compression image watermarking method and system based on spectrum-airspace decoupling, and belongs to the field of information security. Firstly, the watermark information is mapped and remodeled; a watermark encoder based on multi-granularity spectrum-spatial domain feature decoupling is constructed, discrete cosine transform is introduced to filter out high-frequency components, Haar wavelet transform is adopted to realize lossless downsampling, a fast Fourier transform dynamic filter is combined to capture global semantic features, and local texture details are combined through multi-scale spatial domain volume accumulation; designing a physical perception and visual self-adaptive dual embedding strategy, and anchoring watermark energy to an anti-compression brightness channel; constructing an anti-attack layer containing differentiable JPEG compression simulation and mixed noise simulation, and participating in network training; and constructing a decoder and designing a loss function to carry out network optimization. According to the method, the robustness of the watermark under strong compression and complex black box attacks is improved, and extremely high visual imperceptibility is realized through physical and visual constraints.

The invention relates to the technical field of medical image processing, in particular to a CT radiography intelligent imaging method and system based on DCT-GAN multi-scale fusion, and the method comprises the steps: obtaining a preprocessed target NCCT-CTA image data set; dividing according to a preset proportion based on the target NCCT-CTA image data set to obtain a training set and a verification set; the training set and the verification set are input into an initial generative adversarial network based on DCT-GAN multi-scale fusion for model training, in the training process, a virtual CTA image is generated by a generator, an input original CTA image and the virtual CTA image are discriminated in an image domain and a frequency domain subjected to discrete cosine transform processing by a discriminator, and a model is obtained. Determining a network overall loss function based on the similarity difference between the image domain and the frequency domain and the discrimination difference of the discriminator on the two domains; and inputting the preprocessed real-time NCCT image into the trained target generative adversarial network to generate a high-quality and high-resolution virtual CTA image.

The invention discloses a DCT (Discrete Cosine Transform) frequency domain watermark embedding optimization method and system based on a potential space, and the method comprises the steps: firstly coding an original image into the potential space, segmenting the original image into small blocks, embedding a single-bit watermark into the frequency domain of each small block through weighted voting, then carrying out the inverse transformation to a spatial domain, merging, and forming a potential representation with a watermark; decoding through a stable diffusion model to generate an image with a watermark; iteratively optimizing the potential representation through a multi-objective loss function until the loss is minimum; and finally, decoding the optimized representation again, linearly mixing with the original image, adjusting a mixing coefficient through binary search, and outputting a final image on the premise of meeting preset image quality. According to the method, a multi-dimensional robustness optimization strategy is combined, the stability and the non-erasable property of the watermark under the attack of the generative model are systematically improved, and the core requirements of AI copyright protection and content traceability are met.

An image compression device includes a discrete cosine transform (DCT) circuit, a quantization noise shaping (QNS) circuit, and an encoder circuit. The DCT circuit performs a DCT on original image data to generate first data. The QNS circuit performs QNS on block data in first data to determine, based on a first coefficient and a second coefficient of the block data, a QNS score of the first coefficient, and replace the first coefficient with the second coefficient when the QNS score is greater than zero so as to generate second data, wherein the second coefficient is obtained by decreasing an absolute value of the first coefficient. The encoder circuit encodes the second data to generate compressed image data.

Example techniques and devices for coding media data are described. An example device for encoding media data includes one or more memories configured to store the media data and one or more processors implemented in circuitry and coupled to the one or more memories. The one or more processors are configured to determine a hyperprior variable of a neural video coder based on the media data. The one or more processors are configured to apply a discrete cosine transform (DCT) to the hyperprior variable to generate DCT coefficients and encode the DCT coefficients.

For compressing data, preprocessing operations are performed on raw input data. A discrete cosine transform is performed on the preprocessed data, and multiple subbands are created, where each subband represents a particular range of frequencies. The subbands are organized into multiple groups, where the multiple groups comprise a first low frequency group, a second low frequency group, and a high frequency group. A latent space representation is generated corresponding to each of the multiple groups of subbands. A first bitstream is created based on the latent space representation, and an alternate representation of the latent space is used for creating a second bitstream, enabling multiple-pass techniques for data compression. The multiple bitstreams may be multiplexed to form a combined bitstream for storage and / or transmission purposes.

For compressing synthetic aperture radar (SAR) images, preprocessing operations are performed on an input SAR image. A discrete cosine transform is performed on the image, and multiple subbands are created, where each subband represents a particular range of frequencies. The subbands are organized into multiple groups, where the multiple groups comprise a first low frequency group, a second low frequency group, and a high frequency group. A latent space representation is generated corresponding to each of the multiple groups of subbands. A first bitstream is created based on the latent space representation, and an alternate representation of the latent space is used for creating a second bitstream, enabling multiple-pass techniques for SAR image data compression, including phase unwrapping for supporting interferometric SAR (InSAR) applications.

The invention discloses a non-intrusive load identification method based on load high-frequency feature imaging, and aims to realize comprehensive optimization of economical efficiency, low-carbon property and stability of a power system. At a power supply inlet end, a high-precision constantan resistor and a high-speed data acquisition card are used for acquiring current and voltage data, frequency domain information is acquired through Fourier transform, and related parameters are calculated to complete feature extraction. Converting the frequency domain features into a two-dimensional image through discrete cosine transform, and synthesizing current and power feature images into an RGB image by using a Laplacian pyramid fusion technology; and finally, selecting a lightweight deep learning model OfficientNet, training by using a fused image, and adjusting weight deviation to identify load characteristics. The method overcomes the defects of the traditional technology in the aspect of load identification, facilitates reasonable distribution of electric power resources, reduction of energy consumption, improvement of energy utilization efficiency and guarantee of stable and reliable operation of an electric power system through accurate identification of the load, and has important application value in the field of electric power system management.

The invention discloses a fresh tea leaf sorting method based on a frequency domain tree topology network, computer equipment and a storage medium. The method covers the complete process of image acquisition, preprocessing, frequency domain decomposition, deep modeling, map construction and classification. Firstly, image quality is improved through color normalization and edge enhancement, and frequency domain tree decomposition is carried out through wavelet transform and discrete cosine transform to extract multi-scale features. And then, fusing long and short range dependent modeling and a residual convolution module to realize multi-level feature representation, and constructing a tree topology attention path and a structure map for simulating a bud-leaf-vein relationship to enhance semantic understanding. A tree structure is adopted to perceive a classification function, and fine-grained classification of single bud, one bud and one leaf, one bud and two leaves and one bud and multiple leaves is achieved. In training, the robustness of the model is improved by combining cross entropy loss, data enhancement and a regularization strategy. The method is high in classification accuracy and good in stability on a plurality of tea image data sets, and the practical level of automatic fresh tea leaf sorting is effectively improved.

A system and method for real time discrete cosine transform image and video processing with convolutional neural network architecture. The system and method transforms degraded inputs into subband images, which are analyzed by a machine learning classification network to identify specific types of blur and compression artifacts. The classification network dynamically adjusts the parameters of separate DC and AC deblurring networks based on its analysis. This adaptive approach optimizes processing for various degradation types, improving the quality of the reconstructed output. The system and method's real-time capability and enhanced adaptability make it suitable for a wide range of imaging and video applications, offering superior performance over traditional methods.

The invention discloses a defense method for resisting an infrared detection confrontation sample attack, and belongs to the technical field of calculation, reckoning or counting. The method comprises the following steps: firstly, partitioning an input infrared image according to a fixed size, executing two-dimensional discrete cosine transform, calculating the mean square difference of the image on each color channel before / after recompression, then converging into a frequency spectrum heat map, and binarizing to obtain a rough mask for positioning an adversarial patch; inputting the original image into the image segmentation model, calculating the intersection-union ratio of the segmentation mask and the rough mask, removing redundancy, and fusing to generate a final patch area mask; then, according to the patch area mask, original image pixels are removed, and a general image completion algorithm is called to recover a removed area; and finally, sending the complemented image into a deep learning target detector to realize robust detection under the infrared confrontation sample attack. According to the method, defense is carried out on adversarial sample attacks under infrared detection for the first time, and adversarial samples are effectively defended by adopting adversarial sample positioning, image segmentation and image restoration.

The invention discloses a model fingerprint intellectual property protection method based on a frequency visual angle, and the method comprises the steps: extracting the frequency domain information of a model weight: S1, extracting a 3D array of a convolution kernel weight of a bottom convolution layer of a model, and obtaining a frequency domain coefficient through 3D discrete cosine transform (3DDCT); s2, the model owner extracts low-intermediate frequency components in the frequency domain coefficient according to a preset threshold value, and orderly connects the low-intermediate frequency components into a feature vector; s3, using a random projection technology to bind the feature vector and the identity information to generate a fingerprint, and submitting the fingerprint to a trusted third party (TTP) for timestamp verification; and S4, performing suspicious model detection: repeating the above steps on the suspicious model to obtain a feature vector, performing calculation by using an identity matrix of a model owner to obtain a fingerprint of the suspicious model, comparing the fingerprint with a fingerprint in a TTP database, and judging whether the suspicious model is pirated or not through cosine similarity. According to the method, the intellectual property protection capability of the model is effectively improved, and relatively high robustness is shown.

The invention discloses a method for detecting tiny defects of a high-density integrated circuit packaging substrate. The method comprises the following steps: acquiring image data of the surface of the high-density integrated circuit packaging substrate; processing the acquired images in sequence by adopting a mean filtering method, morphological dilation and a texture operator; processing the image by adopting an image binarization processing technology to obtain a binarized image; dividing the binary image into a plurality of energy regions, and calculating an entropy difference through the divided energy regions; performing processing operation on the image data through a discrete cosine transform technology to obtain a DCT coefficient, dividing the DCT coefficient into a high-frequency component and a low-frequency component, and performing calculation to obtain a ratio of a low-frequency coefficient mean value to a high-frequency coefficient mean value; the feature extraction model extracts defect shape features based on the entropy difference and the ratio; comparing a similarity measurement index calculated according to the defect shape characteristics of the packaging substrate with a preset threshold value, and judging whether the packaging substrate has a shape defect or not; and precise detection of small defects of the high-density integrated circuit packaging substrate is realized.

The invention discloses an urban water supply prediction method based on a non-stationary perceptual Transform-BiLSTM model, and the method comprises the steps: independently calculating the mean value and variance of each sequence sample as non-stationary statistical information through introducing a reversible normalization mechanism, enabling a sequence to depend on weight learning reversible mapping on the basis of maintaining the original distribution characteristics of data, and carrying out the calculation of the mean value and variance of each sequence sample as the non-stationary statistical information; and the perception capability of non-stationary components is enhanced, and the training stability is improved. In addition, discrete cosine transform (DCT) is adopted for frequency domain modeling, and the feature extraction capacity of the model for the periodicity and trend of the urban water supply sequence is enhanced. In the aspect of feature modeling, the model fuses the global attention mechanism of Transform and the time sequence modeling capability of BiLSTM, cooperatively captures the long-term dependency relationship and local time sequence dependency features in urban water supply data, and has a remarkable capturing capability effect on the change of a non-stationary structure under the background of multi-scale fluctuation of an urban water supply sequence.

The invention discloses a target segmentation system, method and device based on an attention mechanism. The system comprises an image acquisition device for acquiring a CT scanning image to be segmented; the image processing equipment is used for segmenting an organ abnormal region by adopting an image segmentation model based on an attention mechanism; the image display equipment is used for displaying the CT scanning image and the mark information; the image segmentation model comprises an image enhancement network, which is used for enhancing boundary high-frequency information and texture details of input by using discrete cosine transform (DCT); the main encoder is used for extracting multi-scale global features and deep global semantic information of the CT scanning image; according to the brain area network, local differentiation features are extracted for the focus through a dynamic adaptation strategy by adopting a partition cooperation mechanism; and the decoder is used for recovering space structure and boundary details by adopting a bilinear interpolation up-sampling and multi-scale feature fusion mechanism on the global features and the local differentiation features. The technical problem that the accuracy of image segmentation of lesions such as lungs is poor in correlation is solved.

The invention relates to the technical field of speech recognition and analysis, in particular to a speech recognition and analysis method based on an MFCC algorithm and a VQ-HMM algorithm, and the method comprises the steps: carrying out the preprocessing of an input speech signal; performing feature extraction on the preprocessed voice signal by using an MFCC algorithm; the method comprises the following steps of: framing a voice signal by using an MFCC algorithm, filtering each frame by using a Mel filter bank, then converting a filtering result into a Mel frequency scale, and extracting a cepstrum coefficient through discrete cosine transform (DCT); training a speech recognition model by using a VQ-HMM algorithm; according to the VQ-HMM algorithm, vector quantization is carried out on MFCC feature vectors, continuous feature vector sequences are mapped into discrete symbol sequences, and then a hidden Markov model is used for modeling the symbol sequences; the method has the advantages of being high in feature representation capacity, high in modeling capacity, low in training data requirement, high in robustness, good in interpretability and particularly suitable for small-scale data sets and scenes with high interpretability requirements.

A size of a transform block is identified. A transform type for the transform block is identified. The transform type includes a horizontal transform type and a vertical transform type. Identifying the transform type includes determining whether the size of the transform block is below a predefined block size; and, in response to determining that the size is below the predefined block size, selecting a default transform type for each of the horizontal transform type and the vertical transform type. The transform type is then applied to the transform block. The default transform type can be the discrete cosine transform (DCT).

The embodiment of the invention discloses an image decoding method, an image coding method, electronic equipment and a readable storage medium. The decoding method comprises the following steps: during image decoding, performing first decoding sub-stage processing on a to-be-decoded image code stream to obtain pixel residual information; wherein the first decoding sub-stage comprises the steps of code stream acquisition and entropy decoding of an image to be decoded; caching the pixel residual error information to a shared macro block information buffer; when an image is decoded, acquiring residual information of pixels to be processed in the macro block information buffer by adopting an asynchronous independent clock; performing second decoding sub-stage processing on the to-be-processed pixel residual information to obtain image decoding information; wherein the second decoding sub-stage comprises inverse quantization, inverse discrete cosine transform and pixel code stream write-back. By decoupling the decoding process, asynchronous parallel processing of the decoding process can be realized, the image decoding efficiency is improved, and the memory utilization rate of equipment is improved.