7results about How to "Improve subjective quality" patented technology

The present invention discloses a joint optimization method for compression and reconstruction of ultra-high-definition video, and belongs to the technical field of ultra-high-definition video compression and reconstruction. The technical problem of blind reconstruction caused by the separation of ultra-high-definition video compression and reconstruction process due to the inability to obtain effective information of ultra-high-definition video at the decoding end is solved. The technical scheme is as follows: It includes the following steps: S1, establishing a bidirectional mapping relationship between ultra-high-definition video compression and reconstruction based on mathematical modeling; S2, designing an ultra-high-definition video spatiotemporal feature map information enhancement interaction mechanism based on Transformer; S3, constructing an ultra-high-definition video compression and reconstruction integrated framework based on the spatiotemporal feature map information enhancement interaction mechanism. The beneficial effects of the present invention are as follows: The present invention is oriented to ultra-high-definition video, breaks through the original video encoding and decoding framework, and realizes the joint optimization of ultra-high-definition video compression and reconstruction from the perspective of architectural design.

The invention relates to an image enhancement model training method and device, an image processing method and device, electronic equipment and a computer program product, and belongs to the technical field of image processing. The training method of the image enhancement model comprises the following steps: acquiring an input sample image and a corresponding enhanced sample image; inputting the input sample image into a first enhancement model to obtain a first output sample image, and inputting the input sample image into a second enhancement model to obtain a second output sample image; obtaining a model output quality difference according to the first output sample image, the second output sample image and the enhanced sample image; obtaining a multi-order joint variance according to the input sample image and the first output sample image, and obtaining quality constraint loss according to the multi-order joint variance and the output quality difference; and updating model parameters of the first enhancement model based on the quality constraint loss to obtain an image enhancement model. By calculating the quality constraint loss, the image enhancement quality can be effectively improved.

The application discloses a target distance adaptive image compression method and an image decompression method, wherein the image compression method comprises a training process and an inference process; the training process further comprises depth information extraction, data preprocessing and model training; the depth information extraction takes an image containing a close-range target as input, uses a depth estimation network to extract a depth value corresponding to each pixel from the image, and outputs a single-channel depth map containing the depth value, the resolution of the depth map being the same as that of the input image; the data preprocessing randomly divides images used for training into smaller image blocks, and divides a depth map obtained through the depth information extraction process into small blocks corresponding to the images; the model training performs training on an image compression network, takes the cut image blocks as input, processes the image blocks through a neural network, and outputs corresponding reconstructed image blocks; and the inference process performs image compression.

A method for obtaining a current block of a motion compensated prediction sample is provided. The method comprises selecting (s902) a motion vector from a set of candidate motion vectors, the set of candidate motion vectors comprising a first candidate motion vector and a second candidate motion vector. Selecting a motion vector from a set of candidate motion vectors includes calculating a first misaligned distortion value for a first candidate motion vector. Calculating the first misaligned distortion value for the first candidate motion vector is based on a difference in sample values on a boundary between the first neighboring block and the current block. The method comprises using (s904) the selected motion vector to generate a block of motion compensated prediction samples.

The present disclosure provides a video decoding method, comprising: a step of obtaining a transform skip mode flag indicating whether a transform skip mode can be applied to a current block; a step of determining that a secondary transform / inverse transform can be applied to the current block when the transform skip mode can be applied to the current block according to the transform skip mode flag; and a step of obtaining a transform matrix index for a secondary transform / inverse transform applicable to the current block when the transform skip mode can be applied to the current block according to the transform skip mode flag, and a step of determining whether a secondary transform / inverse transform can be applied to the current block according to the transform matrix index.

An audio signal processing device comprises a discontinuity detector configured to determine an occurrence of a discontinuity from a sudden increase of an amplitude of decoded audio obtained by decoding the first audio packet which is received correctly after an occurrence of a packet loss, and a discontinuity corrector for correcting the discontinuity of the decoded audio.

The application provides a video coding method based on saliency, wherein the coding method comprises the following steps: judging whether a current video image is the first frame in an image group; if yes, using an intra-frame coding method to code the current video image into a code stream BS; if no, obtaining a reference image from a decoded image buffer, and using an inter-frame coding method to code the current video image into a motion vector code stream BS mv and a prediction residual code stream BS r The application has the advantages that, under the same code rate, the video coded by the method of the application has better quality in the salient region, so that the reconstructed video has better subjective quality, or under the same subjective quality, the video code rate is lower, and resources are saved.