9 results about "Entropy estimation" patented technology

The invention discloses a Mama-based entropy model and an image compression method, and mainly solves the problem of limited compression performance caused by poor entropy estimation precision in the prior art. The scheme comprises the following steps: 1) constructing a two-dimensional state space hyper-prior network by a hyper-prior encoder and a decoder to obtain global hyper-prior features; 2) adopting a double-branch gating architecture to obtain a mixed context feature containing local and global dependency information; 3) constructing an entropy parameter fusion and probability modeling network, carrying out channel splicing and feature fusion on the mixed context features and global super-prior features, and outputting a conditional Gaussian distribution parameter of each latent variable position; according to the method, the entropy model estimation precision can be remarkably improved, meanwhile, the compression rate distortion performance is improved, and the balance between the rate distortion performance and the calculation complexity is achieved.

This invention discloses a small-sample Bayesian optimization sampling method and system based on cloud droplet data augmentation, belonging to the field of transportation engineering material design. Addressing the problems of limited initial samples and poor fit of the Bayesian optimization surrogate model in modified asphalt formulation design, this invention acquires initial small-sample data; constructs a clustering cloud model to obtain expectation, entropy estimates, and hyperentropy estimates; generates cloud droplet virtual samples using a forward cloud generator and merges them with the original samples to expand the dataset; trains a Gaussian process regression surrogate model based on the expanded data; constructs an expectation-improved acquisition function to optimize and solve candidate formulations; updates the data after physical testing verification; and iterates repeatedly until the termination condition is met to output the optimal formulation. This invention mines the distribution information of small-sample data through cloud droplet data augmentation, improves the accuracy and sampling efficiency of the surrogate model, significantly reduces the number of expensive physical tests, and lowers the cost of formulation design. It can be widely applied to the formulation optimization of modified asphalt and similar high-cost experimental materials.

A method for determining a threshold for entropy coding that skips potential features in image and video coding using a neural network is described. A threshold for estimating a mean of standard deviations based on all latent variables is proposed. For an autoregressive neural network, in order to avoid drifting between context model parameter estimations calculated during training and reasoning, two separate entropy parameter estimation networks, namely an initial entropy estimation network and a refined entropy estimation network, are adopted, the initial entropy estimation network is used for entropy skipping coding and decoding of quantized latent variables, and the refined entropy estimation network is used for entropy skipping coding and decoding of quantized latent variables. A refined entropy estimation network is used for arithmetic encoding and decoding of quantized latent variables. Methods and systems for multi-stage entropy skipping are also presented.

The invention relates to a self-adaptive quantum random number generation method based on real-time entropy estimation, and belongs to the technical field of quantum random number generation. According to an existing integrated quantum random number generator, due to the non-stationary characteristic of a physical entropy source, the entropy rate dynamically drifts, and after-processing parameters are fixed, randomness safety and generation efficiency are difficult to guarantee at the same time. According to the method, the state transition probability of the original random bit stream is counted in real time, the confidence interval correction is introduced to calculate the minimum entropy, the input bit width is dynamically adjusted accordingly, the Toeplitz matrix is reconstructed for random extraction, and self-adaptive adjustment of the compression ratio is achieved. According to the method, the compression ratio is increased to ensure the security of the output random number when the entropy source quality is reduced, and the compression ratio is reduced to improve the generation efficiency when the entropy source quality is improved, so that the dynamic balance between the randomness security and the generation efficiency is realized on the premise of keeping integration and low cost.

It achieves both high compression ratio and fast processing speed. [Solution] The parallel processing device of the data compression / decompression system divides the original data into multiple data units in predetermined units and stores the multiple data units in the memory. The parallel processing device then generates probability distributions for the multiple data units in parallel using an entropy estimater based on a neural network that predicts the probability distributions of the multiple data units, and generates a cumulative distribution function for the probability distributions corresponding to the multiple data units in parallel. The parallel processing device then performs entropy coding processing in parallel on the multiple data units based on the probability distributions and cumulative distribution functions to generate multiple coded data units, and creates compressed data from the multiple coded data units.

This invention belongs to the field of image processing technology, specifically, it is an image processing method for the diagnosis of congestive heart failure (CHF). First, the electrocardiogram (ECG) data is segmented and denoised. Second, the segmented data undergoes Fast Fourier Transform (FFT) to obtain the corresponding Doppler spectrum sequence. Then, GAF is applied to encode the Doppler spectrum sequence into GASF and GADF images. Next, features are extracted from the two-dimensional images using an improved two-dimensional multi-scale entropy method. Finally, the features are sent to an ELM classifier for classification. The improved two-dimensional multi-scale entropy algorithm disclosed in this invention can improve the accuracy of entropy estimation, reduce the probability of inducing undefined entropy, and more clearly reflect the multi-scale entropy features at each scale. By utilizing ELM to mine deeper information about CHF, it can distinguish between normal and CHF patients, providing diagnostic assistance to cardiologists through a more objective and faster interpretation of ECG signals.

A method for determining thresholds for skipping entropy coding of latent features in image and video coding using neural networks is described. A threshold based on the mean of the estimated standard deviations of all latents is proposed. For autoregressive neural networks, it is proposed to have two separate entropy parameter estimation networks to avoid drift between context model parameter estimates computed during training and inference: an initial entropy estimation network used in entropy skipping of quantized latent coding and decoding, and a refined entropy estimation network used in arithmetic coding and decoding of quantized latents. Methods and systems for multi-stage entropy skipping are also proposed.

This disclosure provides an entropy estimation method and apparatus for image compression. The method includes: obtaining a to-be-processed first feature matrix, where a shape of the first feature matrix is [Cin, hin, win], Cin indicates a quantity of channels of the first feature matrix, hin indicates a height of the first feature matrix, and win indicates a width of the first feature matrix; and inputting the first feature matrix to an entropy estimation network to obtain a second feature matrix, where a shape of the second feature matrix is [Cout, hout, wout], Cout indicates a quantity of channels of the second feature matrix, hout indicates a height of the second feature matrix, and wout indicates a width of the second feature matrix. The second feature matrix and the first feature matrix meet the following conditions: Cout=Cin, hout=s·hin, and wout=s·win, where s is an integer greater than 1.

A method and system for estimating the entropy of bit sequences using a hybrid deep learning model. The system employs a neural network model that integrates statistical and pattern-based analysis to assess entropy. A min-entropy estimation function determines randomness levels, while a convolutional neural network (CNN) extracts sequential patterns. The system leverages a structured training approach with configurable parameters, allowing for adaptive entropy estimation across various applications, including cryptographic security and anomaly detection.