321 results about "Subjective perception" patented technology

3D music visualization process employing a novel method of real-time reconfigurable control of 3D geometry and texture, employing blended control combinations of software oscillators, computer keyboard and mouse, audio spectrum, control recordings and MIDI protocol. The method includes a programmable visual attack, decay, sustain and release (V-ADSR) transfer function applicable to all degrees of freedom of 3D output parameters, enhancing even binary control inputs with continuous and aesthetic spatio-temporal symmetries of behavior. A “Scene Nodes Graph” for authoring content acts as a hierarchical, object-oriented graphical interpreter for defining 3D models and their textures, as well as flexibly defining how the control source blend(s) are connected or “Routed” to those objects. An “Auto-Builder” simplifies Scene construction by auto-inserting and auto-routing Scene Objects. The Scene Nodes Graph also includes means for real-time modification of the control scheme structure itself, and supports direct real-time keyboard / mouse adjustment to all parameters of all input control sources and all output objects. Dynamic control schemes are also supported such as control sources modifying the Routing and parameters of other control sources. Auto-scene-creator feature allows automatic scene creation by exploiting the maximum threshold of visualizer set of variables to create a nearly infinite set of scenes. A Realtime-Network-Updater feature allows multiple local and / or remote users to simultaneously co-create scenes in real-time and effect the changes in a networked community environment where in universal variables are interactively updated in real-time thus enabling scene co-creation in a global environment. In terms of the human subjective perception, the method creates, enhances and amplifies multiple forms of both passive and interactive synesthesia. The method utilizes transfer functions providing multiple forms of applied symmetry in the control feedback process yielding an increased level of perceived visual harmony and beauty. The method enables a substantially increased number of both passive and human-interactive interpenetrating control / feedback processes that may be simultaneously employed within the same audio-visual perceptual space, while maintaining distinct recognition of each, and reducing the threshold of human ergonomic effort required to distinguish them even when so coexistent. Taken together, these novel features of the invention can be employed (by means of considered Scene content construction) to realize an increased density of “orthogonal features” in cybernetic multimedia content. This furthermore increases the maximum number of human players who can simultaneously participate in shared interactive music visualization content while each still retaining relatively clear perception of their own control / feedback parameters.

A method and system for the estimation of the effect of packet loss on a viewer's subjective perception of a packet video stream. The method involves estimating a quality metric for every frame in the video stream, accumulating such metrics over time, and transforming the result into a Mean Opinion Score (“MOS”) which represents the viewer's subjective perception of the video stream. The method takes into account various factors such as frame rate, frame types and sizes, decoding algorithms, propagated error rates, and the effect of video content on viewer perception. The method can be used for both encrypted and unencrypted video streams.

The invention discloses a method for evaluating the visual comfort degree of a three-dimensional image. The method comprises the steps that first a three-dimensional saliency map of a right viewpoint image is obtained by extracting a saliency map of the right viewpoint image and a deep saliency map of a right parallax image, then parallax amplitude, parallax gradient and spatial frequency characteristics are extracted according to three-dimensional saliency map weighing, a characteristic vector of the three-dimensional image is obtained, characteristic vectors of all the three-dimensional images in a three-dimensional image set are trained by utilizing support vector regression, finally each three-dimensional image in the three-dimensional image set is tested by utilizing a support vector regression training model obtained through training, and an objective visual comfort degree evaluation predicted value is obtained. The method has the advantages that characteristic vector information of the three-dimensional images has strong stability and can well reflect the variation situation of the visual comfort degree of the three-dimensional images, so that correlation of an objective evaluation result and subjective perception is effectively improved.

The invention relates to a method for optimizing a driver assistance system A, which method comprises the steps of specifying at least one driver assistance system A to be optimized, determining at least one vehicle parameter function that characterizes an operating state of a vehicle and at least one environment parameter function that characterizes the environment of the vehicle, calculating at least one driving situation characteristic value function that characterizes a driving situation of the vehicle, at least on the basis of the at least one vehicle parameter function and / or the at least one environment parameter function, calculating at least one control intervention characteristic value function that characterizes the activity of driver assistance system A, and calculating a correction function that depends on the at least one driving situation characteristic value function and characterizes a subjective perception of the driving situation by at least one vehicle occupant, at least on the basis of the of the at least one control intervention characteristic value function and on the basis of the at least one vehicle parameter function and / or the at least one environment parameter function.

The invention discloses a stereo image quality objective evaluation method based on visual perception. Firstly, a stereo image is divided into a strong edge block, a weak edge block , a flat block and a texture block, and characteristic information reflecting image quality and depth perception of different zone blocks is extracted through computation so as to obtain stereo image characteristic vectors; and then the characteristic vectors of distorted stereo images of the same distortion type in a distorted stereo image set are trained through support vector regression, and each distorted stereo image of the same distortion type is tested by a support vector regression training model to obtain the objective image quality evaluation forecast value of each distorted stereo image. The method has the advantages that the obtained characteristic vector information reflecting the image quality and the depth perception has stronger stability and can better reflect the quality change condition of the stereo images, and the relevance of an objective evaluation result and subjective perception is improved.

The invention discloses a multi-media playing device dynamically varying an outer shell color along with rhythm. The device comprises an outer shell, a light-emitting unit arranged on the outer shell, a control unit used for controlling the light-emitting unit and an audio decoding unit which decodes audio signals played by the multi-media playing device in real time, quantizing the obtained decoded signals and converting the signals into control signals, and the control unit controls the light-emitting mode of the light-emitting unit according to the control signals. A music dynamic effect is expressed on lamp light outside the device, and the multi-media playing device has the advantages that visual effects of all kinds of audios are expanded to the outer shell of the device to be achieved, so that the visualization effect is enhanced; the lamp light outside the device is changed to be in a dynamic light-emitting mode from a static light-emitting mode, and visual perception which is brought to a user by the colorful lamp light is sufficiently enhanced; the shade and color of the lamp light outside the device are combined with the played audios, so that the expressive force of the device is enhanced, and stronger subjective perception is brought to the user.

The invention discloses a non-reference image quality objective evaluation method based on deep learning. Multi-resolution pyramid and Gaussian difference decomposition is performed on distortion images to be evaluated and then natural statistical characteristics can be extracted by performing simple local normalization on sub-band images without extracting characteristics from a transform domain so that complexity is greatly reduced. Degree of distortion of the images is measured by degree of loss of the natural statistical characteristics with no requirement for reference images or distortion types. The change condition of visual quality of the images under the influence of various image processing and compression methods can be objectively reflected by the method, and evaluation performance of the method is not influenced by the image content or the distortion types and is consistent with subjective perception of human eyes. An existing L moment estimation method is adopted to estimate the distribution parameters of the envelope curve of a gray level histogram, and the distribution parameters obtained through estimation are more accurate and have higher generalization capability.

The invention discloses a method for objectively evaluating image quality. The method comprises the following steps of: dividing a reference image and a distorted image into an edge region, a texture region and a planar region in a combined way according to gradient amplitude information of each pixel point in the reference image and the distorted image; performing phase quantization coding on the reference image and the distorted image according to a gradient phase and a gradient amplitude of each pixel point in each region; calculating hamming distances for the phase quantization coding of different regions of the reference image and the distorted image; and giving different weights to the hamming distances of the different regions according to a human visual system so as to acquire a final object score of image quality evaluation. In the method, since the influence of variation of the distorted image relative to the gradient amplitude and the gradient phase of the reference image on the quality of the distorted image is comprehensively considered, an objective evaluation result obtained by the method has high consistency with a human eye subjective perception result; in addition, the method has low calculation complexity and high running speed.

The invention discloses a method for evaluating a stereo image vision comfort level based on machine learning. According to the method, the important vision area mask of stereo images is extracted through the saliency map of a right viewpoint image and a right parallax image, a characteristic vector used for reflecting parallax range characteristics and parallax gradient characteristics and a characteristic vector used for reflecting space frequency characteristics are extracted through the important vision area mask, the characteristic vectors of the stereo images are obtained, training is carried out on all the stereo images in a stereo image set through support vector regression, and finally each stereo image in the stereo image set is tested through a support vector regression training model obtained in training to obtain the vision comfort level evaluation predicted value of each stereo image. The method has the advantages that the obtained characteristic vector information of the stereo images has strong stability and can reflect the vision comfort level changing conditions of the stereo images well, and therefore the correlation of objective evaluation conditions and subjective perception is effectively improved.

The invention discloses a no-reference image quality evaluation method based on a fully convolutional neural network. A full reference image quality evaluation method is adopted to obtain the objective and real quality map of each distorted image in a training set as supervision to train the normalized image of all distorted images in the training set, and an optimal fully convolutional neural network regression training model is obtained; the normalized image of the distorted image to be evaluated is input into the optimal fully convolutional neural network regression training model; the objective quality evaluation prediction quality map of the distorted image to be evaluated is predicted; and the saliency map of the distorted image to be evaluated is used for carrying out weighted pooling on the objective quality evaluation prediction quality map so as to obtain an objective quality evaluation prediction value. Since various features, including full reference features, saliency features and the like, of the distorted image are combined and the features can accurately describe the distorted image, dependency between the objective evaluation result and subjective perception is effectively improved.

The present invention relates to an audio communication system and method with improved acoustic characteristics. The present invention utilizes the Haas effect to simulate a sound picture that gives a better subjective impression of stereo sound compared to the objective stereo image. It provides a system and a method for presenting the stereo image in an optimal way for an associated echo canceller without compromising with the subjective perception of stereo.

The invention discloses a non-reference asymmetric distorted stereo image objective quality assessment method. The method comprises the following steps: firstly, filtering a left viewpoint image and a right viewpoint image of a to-be-assessed asymmetric distorted stereo image respectively by virtue of a Gabor filter and a Gaussian filter so as to obtain corresponding Gabor filter image and Gaussian image; then, establishing a valid mathematical model integrated with asymmetric distorted stereo visual perception characteristics by simulating a human visual system, and obtaining a left and right viewpoint characteristic integrated image of the to-be-assessed asymmetric distorted stereo image according to the mathematical model; then, carrying out local binarization model operation on the left and right viewpoint characteristic integrated image so as to obtain a local binarization model structural characteristic image, and obtaining a histogram statistic characteristic vector by virtue of a histogram statistic method; and finally, according to the histogram statistic characteristic vector, forecasting an objective quality assessment predicted value by virtue support vector regression. The objective quality assessment method disclosed by the invention has the advantage that the correlation between an objective assessment result and subjective perception can be effectively improved.

The invention, which relates to the digital image processing field, provides a visual-saliency-based random image region quality evaluation strategy, so that an evaluation result meets the subjective perception of the human being. According to the technical scheme, the method comprises: (1), Gaussian filtering is carried out on a reference image IR and a distorted image ID respectively and the filtered image RGBs are converted into Lab color space units; (2), for different image sizes r*c, N windows with random sizes are generated randomly; (3), visual saliency calculation is carried out the reference image and the distorted image respectively by using a visual-saliency extraction algorithm and a visual saliency similarity matrix in each random window is obtained; (4), weighting and integration are carried out on the obtained similarity matrixes of all random windows. The method is mainly applied to an image processing occasion.

The invention discloses a no-reference image quality evaluation method based on gradient information. Through deep digging of perception characteristics of human vision for image structure, gradient filtering is performed on a distorted image, so as to obtain an amplitude image and a phase image of the gradient information; a local binary pattern operation is performed on the amplitude image and the phase image so as to obtain a local binary pattern characteristic image of the amplitude image and a local binary pattern characteristic image of the phase image; then condition probability characteristics of all pixel points of different pixel values in the amplitude image and the phase image are gained; and finally, according to the condition probability characteristics, an objective quality evaluation prediction value of the distorted image to be evaluated is predicted by use of support vector regression. The method has the advantages that the impact of gradient structure change on visual quality is fully considered, so that the obtained objective quality evaluation prediction value can accurately reflect the subjective perception quality of human vision, and the correlation between an objective evaluation result and subjective perception can be improved effectively.

The invention discloses a no-reference objective three-dimensional image quality evaluation method based on binocular visual perception. The method comprises the steps of constructing a converging one-eyed image of a distorted three-dimensional image by using an energy gain control model, and constructing left and right disparity images and indefinite left and right images by using left and right viewpoint images; then extracting a curvelet domain feature from the converging one-eyed image, and separately extracting a generalized Gaussian fitting parameter feature and a lognormal distribution fitting parameter feature from the left and right disparity images and the indefinite left and right images, wherein the three features are used as three-dimensional image feature information; and finally, constructing a relation between three-dimensional image features and average subjective scoring differences through support vector regression to obtain an objective quality evaluation predicted value of the distorted three-dimensional image. The method has the advantages that the acquired feature vector of the distorted three-dimensional image has strong stability and can reflect the quality change condition of the distorted three-dimensional image, the objective evaluation has good consistency with subjective perception of human eyes, and the correlation between the objective evaluation result and the subjective perception is improved.

The invention discloses an eye fundus image no-reference quality evaluation method. The method comprises two processes including a training stage and a test stage. The influence of brightness, naturalness and structure layout on eye fundus image quality is considered. A dark channel proportion feature, a bright channel proportion feature, a non-uniform brightness feature, a naturalness quality evaluation score and a structure layout index are extracted to form eigenvectors; and then the eigenvectors of all eye fundus images in a training image set are trained by utilizing support vector regression, and a quality prediction model is built. In the test stage, the eigenvectors of the eye fundus images used for test are calculated, and according to the quality prediction model built in the training stage, prediction is performed to obtain quality objective evaluation prediction values of the eye fundus images. The obtained eigenvector information can better reflect the quality change conditions of the eye fundus images, so that the correlation between objective evaluation results and subjective perception is effectively improved.

The invention discloses an image quality objective evaluation method based on human eye visual characteristics, which comprises the following steps: carrying out the visual weighting to the gradient phase information distortion degree of a distorted image by utilizing the image gradient amplitude information and in combination with the bandpass characteristics and the brightness sensitivity characteristics of contrast sensitivity functions of a human eye visual system; and reflecting the quality change of the image through the weighting distortion degree. The image quality objective evaluation method has the advantages that when the gradient phase information of the distorted image is changed, the effect on the quality of the image is considered; and the gradient amplitude information andthe human eye visual characteristics are combined so as to lead the evaluate results to be consistent with the subjective feeling of people, therefore, the subjective perception results of human eyesare better reflected.

The invention relates to an encoder adaptive adjustment method based on subjective quality evaluation. The method comprises the following steps: step A, through analyzing influences exerted by encoding parameters and configuration conditions on a video stream code rate, establishing an HEVC encoder parameter model; step B, by use of subjective perception of a terminal user for received video quality, establishing a subjective QoE model integrally taking encoder arrangement and network loss factors into consideration; and step C, based on the established subjective QoE model, for video service of multiple users, constructing an adaptive adjustment algorithm maximizing total QoE of all the users under the condition of a limited channel capacity. Compared to the prior art, the method provided by the invention substantially improves user total satisfaction under the condition of multiple users, at the same time, supports resource distribution according to user priorities, and has great significance on improvement of system performance and optimization of network resource distribution.

The invention discloses a reference-free three-dimensional picture quality objective evaluation method based on machine learning. The reference-free three-dimensional picture quality objective evaluation method comprises the steps that a vision dictionary list is structured in a training stage, sparse coefficient matrixes of all subblocks in a left viewpoint picture and all subblocks in a right viewpoint picture are calculated respectively for the left view point picture and the right view point picture of any distortion three-dimensional picture according to the structured vision dictionary list, the characteristic vector of the left viewpoint picture and the characteristic vector of the right viewpoint picture are obtained through the maximum pooling method, characteristic extraction is simple, and the computation complexity is low; a support vector regression training model of the left viewpoint picture and a support vector regression training model of the right viewpoint picture are structured, objective quality evaluation predictive values, corresponding to each characteristic vector in a test sample data set, of the left viewpoint picture and the right viewpoint picture respectively are obtained through prediction, weighing is conducted according to characteristic vector information, the objective quality evaluation prediction value of the three-dimensional picture is obtained, and the correlation between the objective evaluation result and subjective perception is high.

The invention discloses a non-reference image objective quality evaluation method. The non-reference image objective quality evaluation method comprises the following steps: respectively applying Gaussian smoothing gradient filter and laplace operator Gaussian filter to a distorted image by deeply excavating perception characteristic of human vision to image structure to correspondingly obtain a Gaussian smoothing gradient filter image and a laplace operator Gaussian filter image, then respectively carrying out local binaryzation mode operation on the two filter images to obtain respective local binaryzation mode feature images, then calculating respective marginal probability feature and conditional probability feature of the two local binaryzation mode feature images, and finally forecasting on objective quality evaluation predicted value of the to-be-evaluated distorted images by adopting support vector regression according to the marginal probability feature and conditional probability feature. The obtained objective quality evaluation predicted value can accurately reflect subjective perceived quality of human vision and can be used for effectively improving correlation between objective evaluation result and subjective perception.

The invention discloses an objective evaluation method for the quality of stereo images. The method comprises the following steps of: firstly, respectively extracting a characteristic vector of Gaussian Blur distortion level reflecting the image quality, a characteristic vector of white noise distortion level reflecting the image quality, a characteristic vector of JPEG (Joint Photographic Experts Group) distortion level reflecting the image quality and a characteristic vector of JPEG2000 distortion level reflecting the image quality of the stereo images, so as to obtain a characteristic vector of the stereo images; and testing each distorted stereo image of the same distortion type by a support vector regression training model to get an objective quality evaluation predictive value of each distorted stereo image. The method has the advantages that, in the method, based on the influence of different distortion types on the distorted stereo images, the characteristic information of different distortion types is extracted to form the characteristic vector of the stereo images, so that the obtained characteristic information of the stereo images has strong stability and can reflect the quality variation status of the stereo images better, and the relevancy of the objective evaluation result and the subjective perception is improved.

The invention provides an objective evaluation method of the video quality. The objective evaluation method comprises the following steps: 10) segmenting a source video and a video to be detected at the same time point to obtain a video segment; 20) respectively extracting video blocks of a video frame in a video segment from the source video and the video to be detected, calculating the similarity of the corresponding video block according to the space-time textural feature, wherein the space-time textural feature embodies the pixel difference between the pixels; 30) calculating the quality score of the video frame from the video to be detected according to the similarity of the corresponding video block; 40) calculating the quality value of the video segment from the video to be detected according to the quality score of the video frame from the video to be detected, and further calculating the quality score of the video to be detected. By adopting the method, the obtained quality score is more in line with subjective perceptions of people.

The invention relates to a color correction objective assessment method consistent with subjective perception. The method comprises the following steps: Step S1, reading a reference image and a target image and generating a matched image by a scenario mapping algorithm to be used as a new reference image; Step S2, assessing the target image by a contrast similarity assessment method to obtain a contrast similarity assessment result CS; Step S3, assessing the target image by a pixel-value difference assessment method based on average value to obtain a difference value assessment result DV1; Step S4, assessing the target image by a pixel-value difference assessment method of maximum difference fusion to obtain a difference value assessment result DV2; and Step S5, fusing the assessment results CS, DV1 and DV2 by a linear fusion model to obtain a final objective assessment score value V. The method can effectively assess color consistency between images, has good correlation with subjective scoring of users and has accuracy, and can be applied in the field of image mosaic and left and right view colors consistency assessment of three-dimensional images.

The invention discloses an image quality evaluation method based on a combination neural network and a classification neural network. In the training stage, an objective reality quality image of a distortion image obtained by adopting a full-reference image quality evaluation method is adopted as supervision, a normalized image of the distortion image is trained to obtain a combination neural network regression training model for different distortion types; a classification label of the distortion image is adopted as supervision, and the normalized image of the distortion image is trained to obtain a classification neural network training model; in the testing stage, the normalized image of the distortion image to be evaluated is input into the classification neural network training model,and a distortion type is obtained; according to the distortion type, the normalized image is input into the corresponding combination neural network regression training model to obtain an objective quality evaluation prediction quality map, and adopting a saliency map for performing weighing pooling on the objective quality evaluation prediction quality map, and obtaining an objective quality evaluation prediction value. The method has the advantage that the correlation between the objective evaluation result and subjective perception is effectively improved.

The present invention relates to a no-reference quality evaluation method of compression perception recovery images. The method comprises the steps of aiming at the distortion characteristics of the compression perception recovery images, extracting three local characteristics and five global characteristics of the images to evaluate the quality of the image, wherein the local characteristics comprise the image definition, the contrast and the homogeneity, and the global characteristics comprise four characteristics about the naturality of the images extracted by utilizing a generalized Gaussian model to fit the MSCN coefficients and one characteristic obtained by utilizing the singular value decomposition; and finally utilizing a support vector machine to train the above 8 characteristics to obtain a characteristic model, thereby applying the model to predict the quality scores of the images. The evaluation results of the method are better than the results of an international mainstream method, and have the very high consistency with the human eye subjective perception.

The invention discloses a visual perception-based three-dimensional image quality objective evaluation method which comprises the steps of: firstly, obtaining an objective evaluation measurement value of each pixel point through calculating a local phase characteristic and a local amplitude characteristic of each pixel point in left and right viewpoint images of a three-dimensional image, dividing the three-dimensional image into a shielding region, a binocular inhibition region and a binocular fusion region by a region detection method; and secondly, evaluating all regions respectively and fusing evaluation results to obtain a final image quality objective evaluation prediction value. The visual perception-based three-dimensional image quality objective evaluation method has the advantages that the obtained information for reflecting local phase and local amplitude characteristics has strong stability and can well reflect quality change condition of the three-dimensional image, and the image quality evaluation method of the shielding region, the binocular inhibition region and the binocular fusion region can well reflect the visual perception characteristic of the human visual system, and is effectively improved in correlation of objective evaluation result and subjective perception.

The invention relates to a texture feature-based image quality evaluating method without reference. The texture feature-based image quality evaluating method includes the steps of conducting three-scale wavelet transform decomposition for an image I to obtain nine high-frequency detailed subgraphs, calculating a local binary atlas of the image I by using a local binary mode, calculating the gray co-occurrence matrix of the sub-graphs and the local binary atlas respectively to obtain ten Gh, calculating the four feature parameters of the Gh, namely contrast, correlation, energy and homogeneity, and taking the average of the feature parameters of the horizontal detailed wavelet subgraphs and vertical detailed wavelet subgraphs under same wavelet transform decomposition scale to obtain a 24-dimensional eigenvector, and conducting network training to obtain a prediction model. The texture feature-based image quality evaluating method can effectively improve the correlation between the objective evaluation result and the subjective perception.