33results about How to "Reduced prediction residuals" patented technology

The present invention relates to a method for eliminating a blocking effect in a compressed video signal signal. The method of the invention includes: the encoding step of eliminating a blocking effect by compensating the motion of a signal compressed in block unit to be transmitted; the decoding step of restoring the motion compensated video signal to the original video signal by reducing the prediction residual between the motion compensated video signal and the original video signal and the blocking effect; and the post-filtering step of performing post-filtering in a blocking elimination filter in order to eliminate a blocking effect and ring effect remained in the compensated signal. The equation for obtaining the original pixel is made simple by eliminating the remaining blocking effect and ring effect using a loop/post filter. In addition, normalization parameters can be stored in the look-up table and can be detected according to the difference between pixels adjacent to the original pixel and the size of quantizaiton, whereby the equation for restoring the compressed moving video signal, resulting in excessive smoothing, becomes simpler, and filtering control is made possible by a simple equation, thus making a high speed real time video signal processing possible.

The invention discloses a B-frame expansion direct mode oriented to panoramic video coding. The mode comprises three expansion direct modes, i.e. (1) mode A: using the backward motion vector of a corresponding block in a forward reference frame to carry out time-domain scale to obtain the forward and backward motion vectors of the current block; (2) mode B: using the inverse mapping of the backward motion vector of a corresponding block in a backward reference frame to generate a forward motion vector, utilizing the mapped forward motion vector to scale to obtain the forward and backward motion vectors of the current block; and (3) mode C: using the forward motion vector of the corresponding block in the backward reference frame to carry out time-domain scale to obtain the forward and backward motion vectors of the current block. The invention has the advantages of effectively compensating the defects of the prior art, reducing the prediction residual and increasing the probability of selecting the direct mode, thus greatly saving the bit numbers for coding motion vectors and improving the coding performance of panoramic video sequences.

The invention provides a video encoding method, decoding method, encoding device and decoding device. The video encoding method comprises the following steps of receiving an original YUV sequence of a video file, conducting a coordinate conversion processing to pixels in the original YUV sequence, acquiring a target YUV sequence and a coordinate conversion parameter, wherein the coordinate conversion parameter comprises a coordinate conversion flag bit for marking a target coordinate conversion method applied to conduct the coordinate conversion processing to the position of the pixels in the original YUV sequence and a target coordinate conversion rule which corresponds to the coordinate conversion method, conducting the encoding to the target YUV sequence and the coordinate conversion parameter, and acquiring encoding bitstream. By the adoption of the video encoding method, coordinate conversion processing is conducted to the pixels in the original YUV sequence of the video file, the correlation of the acquired target YUV in time domain or space domain is stronger, forecasting residual can be reduced, and encoding performance is improved.

The invention provides a nitrogen dioxide concentration prediction method and system, and belongs to the technical field of air quality monitoring, and the method comprises the steps: obtaining monitoring data such as atmospheric pollution monitoring data, meteorological monitoring data, remote sensing reanalysis meteorological field data and geographic covariable data; a pre-trained random forest model, an extreme gradient boosting tree model and a residual connection-combined gated circulation unit neural network model are used for processing monitoring data respectively to obtain three nitrogen dioxide concentration predicted values, and a weighted average algorithm is combined to calculate a final nitrogen dioxide concentration value. According to the method, multi-source spatio-temporal data are fused, and time and space change modes of nitrogen dioxide are learned; the advantages of different algorithms are combined through integrated learning, so that the stability of the prediction result is improved, the prediction residual error is reduced, and the nitrogen dioxide concentration prediction with wide coverage range, high prediction precision and multiple time sequences is realized; the portability of the machine learning prediction method is ensured, and the method can be directly applied to a new monitoring station with less historical data.

This application relates to a video encoding method and apparatus, a video decoding method and apparatus, a computer device, and a storage medium. The computer device obtains a to-be-encoded current encoded block in a current video frame and then obtains a first reference block corresponding to the current encoded block in a reference video frame. Next, the computer device obtains, within the reference video frame, one or more second reference blocks matching the first reference block, the one or more second reference blocks and the first reference block being similar reference blocks. Finally, the computer device encodes the current encoded block according to the first reference block and the one or more second reference blocks, to obtain encoded data.

The invention relates to a vehicle object multi-level knowledge dictionary construction method for monitoring video compression, which is used for expressing and compressing objects in a monitoring video, and comprises the following steps of extracting texture layer knowledge which comprises common textures of different types of vehicles in a multi-source monitoring video, and modeling the texturelayer knowledge by using a texture dictionary; extracting structural layer knowledge which comprises a common structure of the same type of vehicles, and modeling the structural layer knowledge by using a three-dimensional model; and extracting the personalized layer knowledge, wherein the personalized layer knowledge comprises transient knowledge that a certain vehicle individual keeps stable for a period of time, and using a residual dictionary to model the vehicle individual. According to the method, the multi-level knowledge dictionary of the object is established from the three levels ofthe texture layer, the structure layer and the personality layer, the appearance of the moving object in the image in the complex environment can be better expressed, the information such as edge details is richer, the tracking recognition efficiency of the monitored vehicle can be improved, and the method can be widely applied to the aspects of intelligent transportation and the like.

The invention relates to a method for selecting a bandwidth compression prediction mode. The method comprises the following steps of: selecting a macro block in an image; calculating a first prediction residual error of the macro block by adopting a first prediction mode; calculating a second prediction residual error of the macro block by adopting a second prediction mode; calculating a first residual subjective sum according to the first prediction residual error, and calculating a second residual subjective sum according to the second prediction residual error; and selecting the minimum value in the subjective sum of the first residual error and the subjective sum of the second residual error, and performing bandwidth compression by adopting a prediction mode corresponding to the minimum value. According to the embodiment of the invention, the most suitable prediction mode is selected from multiple prediction modes by adopting a selector, so that the most suitable reference pixel can be found, the minimum prediction residual error is obtained, and the theoretical limit entropy is reduced.

Disclosed are an image prediction method, a coder, a decoder, and a storage medium. The method comprises: obtaining an initial prediction value of an image component to be predicted of the current block in an image by means of a prediction model; and filtering the initial prediction value to obtain a target prediction value of said image component of the current block.

The invention relates to a prediction method of video compression. The method comprises that an image is divided into multiple MBs in the same size, and one MB is selected as an MB to be predicted; the MB to be predicted is predicted in a first prediction mode, and a first residual error subjective sum is obtained; the MB to be predicted is predicted in a second prediction mode, and a second firstresidual error subjective sum is obtained; and the first residual error subjective sum is compared with the second residual error subjective sum to select a final prediction mode of the MB to be predicted. A better prediction method can be selected from the first and second prediction modes via a prediction selection algorithm, and the prediction effect of a complex texture image is further optimized.

The invention relates to a nonlinear texture-oriented intra-frame prediction method and device and a medium, the method is used in an intra-frame prediction module in the field of image and video coding, and the method comprises the following steps: determining a current intra-frame prediction mode; predicting a non-linear texture in the intra prediction module using a prediction model comprising a quadratic function; deriving a position of a reference pixel from a result of predicting the non-linear texture, and a predicted pixel value being interpolated from the reference pixel. According to the invention, the problem of efficient prediction modeling of an image and video coding and decoding standard intra-frame prediction module for nonlinear texture content is solved. According to the invention, the high-fidelity prediction signal close to the original signal can be generated, the prediction residual error is reduced, and the coding efficiency is improved.