Qualcomm Incorporated

Venkata Meher Satchit Anand Kotra

Marta Karczewicz

Jianle Chen

Hongtao Wang

Vadim Seregin

Nan Hu

Auke Joris Wiggers

Dana Kianfar

An example device for decoding video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: decode a current block of the video data to form a decoded block; determine that a current sample of the decoded block neighbors a sample along a virtual boundary in the decoded block and neighbors one or more samples that are not along any virtual boundary in the decoded block; compute band information for cross component sample adaptive offset (CCSAO) for the current sample using at least one of the one or more samples that are not along any virtual boundary in the decoded block and without using the sample along the virtual boundary; and perform CCSAO on the current sample using the band information.

Virtual boundary processing for CCSAO, bilateral filter and adaptive loop filter for video coding

A device for decoding video data determines a pre-filtered reconstructed block of video data; applies one or more of a deblocking filter or a sample adaptive offset filter to the pre-filtered reconstructed block to determine a filtered reconstructed block; applies an adaptive loop filter (ALF) to the filtered reconstructed block to determine a final filtered reconstructed block, wherein to apply the ALF to the filtered reconstructed block, the device is further configured to determine a difference value based on a difference between a value of a current sample of the filtered reconstructed block and a value of a pre-filtered neighboring sample; apply a filter to the difference value to determine a sample modification value; and determine a final filtered sample value based on the sample modification value.

Adaptive loop filter with samples before deblocking filter and samples before sample adaptive offsets

An example device for filtering decoded video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: decode a picture of video data; code a value for a syntax element representing a neural network model to be used to filter a portion of the decoded picture, the value representing an index into a set of pre-defined neural network models, the index corresponding to the neural network model in the set of pre-defined neural network models; and filter the portion of the decoded picture using the neural network model corresponding to the index.

Multiple neural network models for filtering during video coding

An example device for filtering decoded video data includes a memory configured to store video data; and one or more processors implemented in circuitry and configured to: decode a block of video data to form a decoded block; apply a filter to the decoded block to form a filtered block; multiply samples of the filtered block by a scaling factor to form a refined filtered block; and combine samples of the refined filtered block with corresponding samples of the decoded block. The one or more processors may further encode the block prior to decoding the block. The one or more processors may encode or decode a value of a syntax element representing the scaling factor, e.g., in a picture header of a picture including the block.

Filtering process for video coding

An example device for filtering decoded video data includes one or more processors configured to execute a neural network filtering unit to: receive data from one or more other units of the device, the data from the one or more other units of the device being different than data for a decoded picture of video data, and wherein to receive the data from the one or more other units of the device, the one or more processors are configured to execute the neural network filtering unit to receive boundary strength data from a deblocking unit of the device; determine one or more neural network models to be used to filter a portion of the decoded picture; and filter the portion of the decoded picture using the one or more neural network models and the data from the one or more other units of the device, including the boundary strength data.

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Munich, Germany

Venkata Meher Satchit Anand Kotra