The invention relates to a textural feature-based no-reference image quality evaluation method. The method comprises the following specific steps of: dividing a graphic database into a training image set and a test image set; constructing images uder different scales for each image; solving normalized brightness images for the images under different scales; solving normalized gray-level co-occurrence matrixes in four directions for the normalized brightness images; calculating energy, entropies, contrast ratios and dependencies of the normalized gray-level co-occurrence matrixes to combine a feature vector; sending the feature vector and a subjective MOS score to a support vector machine to carry out training; and predicting test images by using the trained support vector machine so as to obtain an objective image quality evaluation result. According to the method, image quality evaluation is carried out by utilizing textural features; compared with the existing algorithms, the method is lower in calculation complexity and convenient to realize in real time, and has the effect of improving the prediction precision through extracting the textural features by utilizing the gray-level co-occurrence matrixes with a plurality of scales and a plurality of angles.

Textural feature-based no-reference image quality evaluation method

The invention discloses a similar image compression method based on residual compression sensing. The method comprises the steps of 1) collecting a group of similar images; 2) performing intelligent training on the similar images to extract a common template, subtracting a first image to be compressed from the common template to obtain a residual, training the residual through the K-SVD (K-singular value decomposition) algorithm to obtain a common dictionary and a measurement value coefficient, performing compression sensing measurement on the residual to obtain a measured residual value, extracting the nonzero terms of the measured value for quantization and entropy coding to obtain a compression code stream, and extracting the position information of the nonzero terms as the header information; 3) perform anti-entropy coding and inverse quantization on the compression code stream to obtain a nonzero term residual measured value, restoring a measurement matrix by combining the header information, according to the dictionary, reconstructing a residual image through the OMP (orthogonal matching pursuit) sparse coding algorithm, and adding the residual image to the common temperate to obtain a reconstructed image; 4) outputting the reconstructed image. The similar image compression method based on residual compression sensing can greatly reduce data storage, effectively improve the compression efficiency and achieve high reconstruction quality.

Similar image compression method based on residual compression sensing

The invention relates to a no-reference screen image quality evaluation method based on support vector regression. A direction gradient histogram is calculated as feature information of a distorted screen image. A mapping relation model of screen image feature information and subjective quality scores is obtained through training using a support vector regression (SVR) network, in order to accurately evaluate the quality of a screen image. The method of the invention needs no reference screen image. The calculated objective score is highly consistent with the visual perception of the human eyes, and the calculation is simple.

No-reference screen image quality evaluation method based on support vector regression

The invention relates to a desert seismic signal denoising method based on the VMD approximate entropy and a multi-layer perceptron and belongs to the field of geophysical technology. The two-dimensional desert seismic record is subjected to variational mode decomposition to obtain a series of eigenmode components, the approximate entropy of each eigenmode component is calculated, all the eigenmode components are respectively divided into an effective signal dominant component and a noise dominant component, a characteristic quantity is constructed through effective signal correlation, the characteristic quantity is inputted into the multi-layer perceptron for classification, the valid signal portion determined by the multi-layer perceptron classifier is reserved, the noise portion determined by the multi-layer perceptron classifier is removed, the noise dominant component is denoised through combining an autocorrelation coefficient with tzhe multi-layer perceptron, and lastly, desertseismic signal denoising is realized through reconstruction. The desert seismic signal denoising method is advantaged in that noise removal under strong noise and low signal to noise ratio conditionsis achieved, the desert seismic signal denoising method has fast speed, high accuracy and strong anti-interference ability, and denoising of desert seismic signals under low frequency and low SNR canbe realized.

Desert seismic signal denoising method based on VMD approximate entropy and multi-layer perceptron

The present invention discloses a stereoscopic image quality objective evaluation method. Response output after lateral geniculate nucleus processing of each pixel in each of a left viewpoint image and a right viewpoint image of an undistorted stereoscopic image and a distorted stereoscopic image, response output after primary monocular visual cortex processing, response output on different parallax planes after primary binocular visual cortex processing of pixels at the same coordinate position in the left viewpoint image and the right viewpoint image of the distortion stereoscopic image and pixels at the same coordinate position in the left viewpoint image and the right viewpoint image of the undistorted stereoscopic image, and response output on different parallax planes after second binocular visual cortex processing of the pixels at the same coordinate position in the left viewpoint image and the right viewpoint image of the distortion stereoscopic image and the pixels at the same coordinate position in the left viewpoint image and the right viewpoint image of the undistorted stereoscopic image are calculated, so as to obtain a quality vector of the distorted stereoscopic image. Through adoption of the method, the quality change of the distorted stereoscopic image can be well reflected, and a correlation between an objective evaluation result and subjective perception is effectively improved.

Stereoscopic image quality objective evaluation method

The invention discloses an improved natural scene statistic model-based no-reference image quality evaluation method, and mainly aims at solving the problem that the evaluation carried out on noise images by computers does not accord with human eye perception. The method is realized through the following steps of: 1, giving a training set which comprises various types of noise images, and extracting characteristics of each noise image; 2, respectively training a classification model and a regression model by adopting a support vector machine according to the characteristics, noise types and subjective quality value of the noise images in the training set; 3, calculating characteristics of a test image; 4, calculating a probability that the test image belongs to each noise type according to the classification model and the characteristics of the test image; 5, calculating a quality value of the test image when the test image belongs to each noise type according to the regression model and the characteristics of the test image; 6, calculating a final quality of the test image; and 7, obtaining the quality evaluation of the test image according to the final quality value. According to the method, the evaluation result is in accordance with the human eye perception, so that the method can be used for the correct quality identification of natural images and remote sensing images.

Improved natural scene statistic model-based no-reference image quality evaluation method

The invention discloses an image compression method based on vision redundancy measurement. The image compression method mainly solves the problem that an existing image compression method is low in image compression efficiency. The image compression method includes the steps: (1) forecasting an image to be compressed through an autoregression model to obtain a forecasted image, (2) obtaining a residual image according to the image to be compressed and the forecasted image, and calculating local binary pattern values of pixels in the residual image, (3) counting the occurring probability of the same local binary pattern values in the neighbor areas around the pixels, and calculating the uncertainty of the image to be compressed, (4) calculating visual threshold values corresponding to the pixels according to the uncertainty to conduct redundancy removal operation on the image to be compressed to obtain the pre-processed image, and (5) compressing the pre-processed image to obtain a compressed image. According to the image compression method, vision redundancy information in the image can be effectively removed through the visual threshold values, the compression efficiency is promoted, and the image compression method can be used for transmitting online video, mobile phone picture-phone images and satellite remote sensing images.

Image compression method based on vision redundancy measurement

The invention discloses a sub-band-information-entropy-measure-based image quality evaluation method. With the method, a problem that evaluation of a noise image by a computer does not conform to the perception of the human eyes can be solved. The method comprises: step one, block discrete cosine transform is carried out on a to-be-tested image; step two, similarity frequency band coefficients of all sub block transform coefficients are combined; step three, the combined coefficients are reconstructed and the identical frequency band coefficients form reconstruction sets; step four, an information entropy of each reconstruction set coefficient is calculated; step five, the operations from the step to step four are carried out on a reference image corresponding to the to-be-tested image to obtain an information entropy of the reference image; step six, differences of information contents on all frequency bands of the to-be-tested image and the reference image are calculated; step seven, a weighted sum of all frequency band information content differences is calculated to obtain a quality value of the to-be-tested image; and step eight, according to the quality value, quality evaluation of the to-be-tested image is obtained. According to the invention, the obtained evaluation result conforms to the perception of the human eyes. The method can be used for identifying image transmission and image retrieving on the internet and measuring the quality of the image processing index.

Sub-band-information-entropy-measure-based image quality evaluation method

The invention discloses a characteristic dictionary-based no-reference image quality evaluation method, and mainly aims at solving the problem that the evaluation carried out on noise images by computers does not accord with human eye perception. The method is realized through the following steps of: 1, randomly selecting 60 natural images and calculating a local mode of each pixel in all the images; 2, clustering the local modes by adopting a K-means method, and forming a characteristic dictionary by a clustering center; 3, extracting characteristics of each training image in a training sample set on the basis of the characteristic dictionary; 4, training a regression model through support vector regression according to the characteristics of the training images and corresponding subjective quality values; 5, extracting characteristics of a test image on the basis of the characteristic dictionary; 6, inputting the characteristics of the test image into the regression model and predicting the image quality; and 7, obtaining the quality evaluation of the test image according to a quality value. According to the method, the evaluation result is in accordance with the human eye perception, so that the method can be used for identifying the quality of natural images, remote sensing images and the like, and measuring the performance of imaging equipment.

Characteristic dictionary-based no-reference image quality evaluation method

The invention discloses a partial reference image quality evaluation method based on an orientation selection characteristic, and mainly solves the problem that limited experimental data cannot be utilized to accurately evaluate noise image quality at present The implementation steps include: 1 simulating a space structure distribution characteristic of image pixel points according to an orientation selection principle of the optic nerve; 2 determining the space structure distribution of pixel points through by an azimuth angle difference value between the pixel points and a set threshold value; 3 reducing the space structure distribution of all the pixel points to modes based on a direction selection characteristic, making statistics of the distribution number of space structures in an image which belong to a certain orientation selection characteristic mode and mapping to a histogram based on the orientation selection characteristic; and 4 using a reference image and the histogram of a noise image to evaluate the quality of an image to be processed The partial reference image quality evaluation method based on the orientation selection characteristic can accurately evaluate the quality of the noise image by simulating sensitivity of the human optic nerve to orientation, and can be used for image transmission, image retrieval, image compression and identification of image quality on the Internet

Shi Guangming

Papers

Improved natural scene statistic model-based no-reference image quality evaluation method

Image compression method based on vision redundancy measurement

Sub-band-information-entropy-measure-based image quality evaluation method

Characteristic dictionary-based no-reference image quality evaluation method

Partial reference image quality evaluation method based on orientation selection characteristic