479 results about "Image prediction" patented technology

The invention provides a method and a device for image prediction. The method comprises the steps of: obtaining a first reference unit of an image unit, wherein the image unit and the first reference unit obtain respective predicted images by using the same affine model; obtaining motion information of basic motion compensation units at the at least two preset positions of the first reference unit; and obtaining the motion information of the basic motion compensation units of the image unit. Therefore, by multiplexing the motion information of the first reference unit which adopts the same affine pre-model, a motion vector of the current image unit is obtained more exactly, the coding and decoding complexity is kept while the prediction accuracy is improved, and the coding and decoding performance is improved.

The embodiment of the invention discloses an image prediction method and a relevant device. The image prediction method comprises the following steps: determining the motion vector predictors of K pixel samples in a current image block, wherein K is an integer greater than 1, the K pixel samples comprise a first vertex angle pixel sample of the current image block, the motion vector predictor of the first vertex angle pixel sample is obtained on the basis of the motion vector of a preset first spatial domain adjacent image block of the current image block, and the first spatial domain adjacent image block is adjacent to a first vertex angle pixel sample spatial domain; and predicting the pixel value of the current image block on the basis of a non-translational motion model and the motion vector predictors of the K pixel samples. According to the scheme in the embodiment of the invention, the computation complexity of image prediction based on the non-translational motion model is lowered.

A method and apparatus for determining a prediction mode which is used during encoding of a current block is provided. In the method and apparatus, a prediction mode candidate group to be used for the current block is determined based on a prediction mode for a block positioned at the same location on a reference picture as a location of the current block encoded, predictive coding is performed on the current block by using predictive modes included in the prediction mode candidate group. Thus, image prediction efficient is improved, and the time and number of calculations required for prediction mode determination are reduced.

A video coding or decoding method using inter-image prediction to encode input video data in which each chrominance component has 1/Mth of the horizontal resolution of the luminance component and 1/Nth of the vertical resolution of the luminance component, where M and N are integers equal to 1 or more, comprises: storing one or more images preceding a current image; interpolating a higher resolution version of prediction units of the stored images so that the luminance component of an interpolated prediction unit has a horizontal resolution P times that of the corresponding portion of the stored image and a vertical resolution Q times that of the corresponding portion of the stored image, where P and Q are integers greater than 1; detecting inter-image motion between a current image and the one or more interpolated stored images so as to generate motion vectors between a prediction unit of the current image and areas of the one or more preceding images; and generating a motion compensated prediction of the prediction unit of the current image with respect to an area of an interpolated stored image pointed to by a respective motion vector; in which the interpolating step comprises: applying a ×R horizontal and ×S vertical interpolation filter to the chrominance components of a stored image to generate an interpolated chrominance prediction unit, where R is equal to (U×M×P) and S is equal to (V×N×Q), U and V being integers equal to 1 or more; and subsampling the interpolated chrominance prediction unit, such that its horizontal resolution is divided by a factor of U and its vertical resolution is divided by a factor of V, thereby resulting in a block of MP×NQ samples.

Provided are an image encoding/decoding method and apparatus, in which one of a plurality of color component images is predicted from a different color component image reconstructed using a correlation between the plurality of color component images. Using a reconstructed image of a first color component image selected from among the plurality of color component images forming a single color image, the other color component images are predicted.

A method and device for deblurring an image having pixels. A blurred image is downloaded into a systolic array processor having an array of processing logic blocks such that each pixel arrives in a respective processing logic block of one pixel or small groups of pixels. Data is sequentially exchanged between processing logic blocks by interconnecting each processing logic block with a predefined number of adjacent processing logic blocks, followed by uploading the deblurred image. The processing logic blocks provide an iterative update of the blurred image by (i) providing feedback of the blurred image prediction error using the deblurred image and (ii) providing feedback of the past deblurred image estimate. The iterative update is implemented in the processing logic blocks by u(n+1)=u(n)−K*(H*u(n)−y_b)−S*u(n).

Provided are an image encoding/decoding method and apparatus, in which one of a plurality of color component images of an input image is predicted from a different color component image using a correlation between the color component images and the encoding order of the plurality of color component images is adaptively determined according to the characteristics of the input image. In the image encoding method and apparatus, prediction errors or the amounts of generated bits for the remaining color component images predicted using a previously encoded color component image according to encoding orders are compared to determine the optimal encoding order and encoding is performed using a correlation between the color component images according to the determined encoding order.

It is possible to provide an image prediction/encoding device, an image prediction/encoding method, an image prediction/encoding program, an image prediction/decoding device, an image prediction/decoding method, and an image prediction/decoding program which can select a plurality of candidate prediction signals without increasing an information amount. A weighting device (234) and an adder (235)perform a process (averaging, for example) of pixel signals extracted by a prediction adjacent region acquisition device (232) by using a predetermined synthesis method so as to generate a comparative signal for an adjacent pixel signal for each of combinations. A comparison/selection device (236) selects a combination having a high correlation between a comparison signal generated by the weighting device (234) and an adjacent pixel signal acquired by an object adjacent region acquisition unit (233). A prediction region acquisition unit (204), a weighting unit (205), and an adder (206) generate a candidate prediction signal and process it by using the predetermined synthesis method so as to generate a prediction signal.

An encoding target region in an image can be partitioned into a plurality of prediction regions. Based on prediction information of a neighboring region neighboring a target region, the number of previously-encoded prediction regions in the target region, and previously-encoded prediction information of the target region, a candidate for motion information to be used in generation of a predicted signal of the target prediction region as a next prediction region is selected from previously-encoded motion information of regions neighboring the target prediction region. According to the number of candidates for motion information selected, merging block information to indicate generation of the predicted signal of the target prediction region using the selected candidate for motion information and motion information detected by prediction information estimation means, or either one of the merging block information or the motion information is encoded.

The embodiment of the invention discloses an image prediction method and an associated device. The image prediction method comprises the following steps: determining motion vectors of W control points in a current image block; calculating the motion vectors of P pixel units of the current image block by using a motion model and the motion vectors of the W control points, wherein the precision of the determined motion vectors of the W control points as 1/n pixel precision, the precision of the calculated motion vector of each pixel unit in the P pixel units is 1/N pixel precision, the P pixel units are a part of or all pixel units of the current image block, and N is greater than n; and carrying out interpolation filtering on the pixels of the corresponding pixel units in a reference image of each pixel unit in the P pixel units by using an interpolation filter with a phase number Q to obtain a pixel prediction value of each pixel unit in the P pixel units, wherein Q is greater than n. The technical scheme provided by the embodiment of the invention is conducive to reducing the computational complexity of the image prediction process.

The present invention relates to an image processing device and an image processing method whereby encoding efficiency of image prediction encoding can be improved.

A horizontal processing unit 31 performs horizontal packing as horizontal processing to manipulate the horizontal-direction array of pixels of each of a first thinned-out image and a second thinned-out image arrayed in checkerboard fashion, obtained by thinning out the pixels of each of a first image and a second image different from the first image every other line in an oblique direction, wherein pixels of first and second thinned-out images are packed in the horizontal direction. A combining unit 33 generates a combined image which is combined by adjacently arraying the post-horizontal processing first and second thinned-out images after horizontal processing, as an image to serve as the object of prediction encoding. The present invention can be applied to a case of performing prediction encoding on a first and second image, such as an L (Left) image and R (Right) image making up a 3D image or the like, for example.

Provided is an image processing device for associating images with objects appearing in the images, while reducing burden on the user. The image processing device: stores, for each of events, a photographic attribute indicating a photographic condition predicted to be met with respect to an image photographed in the event; stores an object predicted to appear in an image photographed in the event; extracts from a collection of photographed images a photographic attribute that is common among a predetermined number of photographed images in the collection, based on pieces of photography-related information of the respective photographed images; specifies an object stored for an event corresponding to the extracted photographic attribute; and conducts a process on the collection of photographed images to associate each photographed image containing the specified object with the object.

A high-resolution remote sensing image impervious surface extraction method and system based on deep learning and semantic probability. The method includes: obtaining a high-resolution remote sensingimage of a target region, normalizing image data, dividing the image data into a sample image and a test image; constructing a deep convolutional network, wherein the deep convolutional network is composed of a multi-layer convolution layer, a pooling layer and a corresponding deconvolution and deconvolution layer, and extracting image features of each sample image; predicting each sample image pixel by pixel, and constructing a loss function by using the error between the predicted value and the true value, and updating and training the network parameters; extracting the test image features by the deep convolutional network, and carrying out the pixel-by-pixel classification prediction, then constructing a conditional random field model of the test image by using the semantic associationinformation between pixel points, optimizing the test image prediction results globally, and obtaining the extraction results. The invention can accurately and automatically extract the impervious surface of the remote sensing image, and meets the practical application requirements of urban planning.

The invention discloses an intra-frame image predictive encoding and decoding method and a video codec. The intra-frame image predictive encoding and decoding method comprises the following steps that intra-frame image predictive encoding is conducted by the adoption of an chromaticity intra-frame prediction mode; a reconstruction value of a luminance component Y of a transformation unit (TU) is acquired; a chromaticity component U of the current TU is predicted by the means of the reconstruction value of the luminance component Y of the current TU; a reconstruction value of the chromaticity component U of the current TU is acquired; a chromaticity component V of the current TU is predicted by the means of the reconstruction value of the chromaticity component U of the current TU. According to the intra-frame image predictive encoding and decoding method and the video codec, relevance between the chromaticity component V and the chromaticity component U can be fully used, and therefore predictive encoding and decoding efficiency of intra-frame images is improved.

Inter-color image prediction is based on color grading modeling. Prediction is applied to the efficient coding of images and video signals of high dynamic range. Prediction models may include a color transformation matrix that models hue and saturation color changes and a non-linear function modeling color correction changes. Under the assumption that the color grading process uses a slope, offset, and power (SOP) operations, an example non linear prediction model is presented.

The invention relates to an image prediction method based on a multi-layer convolution long-short-term memory neural network, and the method comprises the steps: extracting the change features of an inputted sequence image through a multi-layer convolution long-short-term memory neural network module, and extracting more abstract features through the deepening of single-layer convolution, therebyimproving the expression capability. In the prediction process, the extracted change features are utilized to predict a next image. The predicted image is input back to the prediction module again, sothat a next image is obtained. A single-layer convolution long-short-term memory neural network of an original method is poor in capability of extracting change features of sequence images, and showsthat structural details of the images output during prediction are fuzzy. According to the method, by deepening the convolution layer, more accurate change features are extracted, and the problem ofstructural detail blurring of the predicted image is obviously improved.

The invention provides a fast video classification method based on images and texts, comprising the following steps of respectively relating images of a plurality of videos with a plurality of video types, and relating texts of the plurality of videos with the plurality of video types, so as to generate an image training set and a text training set corresponding to each video type; respectively extracting image feature information from each image training set to train so as to create an image prediction model, and extracting text feature information from each text training set to train so as to create a text prediction model; respectively extracting the image feature information of a to-be-detected video to predict on the image prediction model, and extracting the text feature information of the to-be-detected video to predict on the text prediction model, and executing or operating the two prediction results as a check out type. The fast video classification method based on the images and texts has the advantage that the fast video classification can be realized.