Abstract: The present disclosure provides a video data processing method. The method includes: receiving a bitstream; decoding a first index from the bitstream; determining a maximum number of an adaptive loop filter (ALF) for a component of a picture based on the first index; and processing pixels in the picture with the ALF.

Abstract: The present disclosure provides systems and methods for performing adaptive resolution change during video encoding and decoding. The methods include: comparing resolutions of a target picture and a first reference picture; in response to the target picture and the first reference picture having different resolutions, resampling the first reference picture to generate a second reference picture; and encoding or decoding the target picture using the second reference picture.

Abstract: The present invention belongs to the field of petrochemical industry, and discloses a method for selective hydrogenation of butadiene extraction tail gas and a selective hydrogenation apparatus thereof.

Abstract: The present disclosure provides a video data processing method for cross-component sample adaptive offset (CCSAO). The method includes determining an index based on a vertical coordinate of a chroma sample within a picture; determining a luma sample based on the index; classifying the chroma sample based on a reconstructed value associated with the luma sample; determining an offset based on the classification; and adding the offset to a reconstructed value associated with the chroma sample.

Abstract: A method for video processing using Cross-Component Adaptive Loop Filter (CCALF) is provided. The method includes filtering decoded video content using a CCALF, wherein the CCALF is a 24-tap 9×9 filter.

Abstract: A video processing method includes: determining whether an inter predictor correction is enabled for a coding block; and when the inter predictor correction is enabled for the coding block, performing the inter predictor correction by: obtaining a plurality of predicted samples from a top boundary and a left boundary of a predicted block corresponding to the coding block; obtaining a plurality of reconstructed samples from top neighboring reconstructed samples and left neighboring reconstructed samples of the coding block; and deriving a corrected predicted block based on the plurality of the predicted samples, the plurality of the reconstructed samples and the predicted block.

Abstract: Systems and methods described herein provide for monitoring of video streaming events. Techniques disclosed include rendering a first omnidirectional video associated with a first viewpoint. In response to a request to access an omnidirectional video associated with another viewpoint, a time of the request and information identifying the other viewpoint is recorded. Techniques disclosed also include identifying a time of switching into rendering of a second omnidirectional video associated with a second viewpoint, and reporting a viewpoint switching latency, representing a difference between the identified switching time and a recorded request time associated with information identifying the second viewpoint.

Abstract: Embodiments of the present disclosure provides systems and methods for processing video content. The methods include: reconstructing a plurality of luma samples associated with a picture; and reconstructing a chroma block associated with the picture. The reconstructing of the chroma block includes: determining whether the chroma block has non-zero residues; and in response to a determination that the chroma block has one or more non-zero chroma residues, determining an averaged value of the plurality of reconstructed luma samples, and scaling residues of the chroma block based on the averaged value, prior to reconstructing the chroma block.

Abstract: The present disclosure provides methods for convolutional-neural-network (CNN) based filter for video coding. An exemplary method includes: applying motion estimation to a target coding block, to determine a reference block of the target coding block; inputting, to a convolutional neural network (CNN) filter, image data associated with the target coding block and the reference block; and executing the CNN filter to determine a residual associated with the target coding block based on the input image data.

Abstract: The present disclosure provides methods for performing training and executing of a multi-density neural network in video processing. An exemplary method comprises: receiving a video stream comprising a plurality of pictures; processing the plurality of pictures using a first branch of a first block in the neural network, wherein the neural network is configured to reduce blocking artifacts in video compression of the video stream and the first branch comprises one or more residual blocks; and processing the plurality of pictures using a second branch of the first block in the neural network, wherein the second branch comprises a down-sampling processing, an up-sampling processing, and one or more residual blocks.

Abstract: The present disclosure provides systems and methods for processing video content. The method can include: receiving a bitstream comprising video content; determining whether a first signal associated with the video content satisfies a given condition; and in response to the determination that the first signal satisfies the given condition, disabling both a cross component adaptive loop filter (CCALF) process and a chroma adaptive loop filter (ALF) process.

Abstract: A method for temporal resampling for multi-task machine vision is provided. The method includes receiving a bitstream of a video sequence after temporal resampling; and constructing a target frame from the bitstream using a frame construction model.

Abstract: A computer-implemented method for training spatial resampling modules includes: down-sampling, by a down-sampling module, an input image data to generate a down-sampled image data; up-sampling, by an up-sampling module, the down-sampled image data to generate a first up-sampled image data; analyzing, by a plurality of analysis models corresponding to a plurality of tasks, the first up-sampled image data; and training the down-sampling module based on a loss function associated with the plurality of analysis models according to the input image data and the first up-sampled image data.

Abstract: Video data may be palette decoded. Data defining a palette table may be received. The palette table may comprise index values corresponding to respective colors. Palette index prediction data may be received and may comprise data indicating index values for at least a portion of a palette index map mapping pixels of the video data to color indices in the palette table. The palette index prediction data may comprise run value data associating run values with index values for at least a portion of a palette index map. A run value may be associated with an escape color index. The palette index map may be generated from the palette index prediction data at least in part by determining whether to adjust an index value of the palette index prediction data based on a last index value. The video data may be reconstructed in accordance with the palette index map.

Abstract: Systems and methods are disclosed including techniques for rendering a 360-degree media content. Techniques disclosed include receiving a 360-degree media content and associated metadata that include a classification of a first spatial region from the received content. Techniques disclosed further include determining that a detected user movement is associated with a rendering of the first spatial region and determining whether the classification associated with the first spatial region complies with a stored user preference. If the classification violates the user preference, a path for gradually shifting the content rendering from a currently rendered spatial region to a spatial region that complies with the user preference is determined, and the received content is rendered according to the determined path.

Abstract: The present disclose provides methods and systems for processing chroma signals. According to some embodiments, the method can include: receiving a sequence parameter set (SPS); and determining, based on a first flag signaled in the SPS, whether chroma residual scaling is enabled or disabled for a slice associated with the SPS.

Abstract: The present disclosure provides systems and methods for processing video content using motion compensation interpolation. The methods include: in response to a target picture and a reference picture having different resolutions, applying a band-pass filter to the reference picture, to perform a motion compensated interpolation with reference down-sampling to generate a reference block; and encoding or decoding a block of the target picture using the reference block.

Abstract: A system may receive an input image block, and input the input image block into multiple models which may be trained using a plurality of different datasets of image blocks. Each model of the multiple models may be trained using a dataset having similar attributes. The system may determine a model having a highest compression efficiency from among the multiple models, and encode the input image block using the determined model.

Abstract: A method for learned image compression is provided. The method may include receiving first image data; downsampling the first image data to second image data; encoding the second image data to third image data, the third image data being a bitstream; decoding the third image data to fourth image data; and reconstructing, as reconstructed image data, the first image data based at least in part on the fourth image data and a feature vector.

Abstract: A VVC-standard encoder and a VVC-standard decoder are provided, implementing application of DMVR on affine merge mode-coded blocks to refine the motion vector accuracy and thereby improve coding efficiency. A refined motion vector (MV) search is performed for a control point motion vector (CPMV) of an inter-coded coding block (CB), outputting a refined MV of the CB. A refined MV search includes deriving a MV of a subblock of the CB based on a CPMV of the CB, performing subblock MV refinement for the MV of the subblock, and outputting the refined MV of the CB based on a refined MV of the subblock. A refined MV search further includes deriving an affine model parameter based on a plurality of CPMVs of the CB, performing an affine parameter offset search for the affine model parameter, and outputting the refined MV of the CB based on an optimal parameter offset.