It is made possible to reduce motion picture quality degradation caused by strobing in a layer of a basic frame frequency and to maintain a high image quality in layers of the other frame frequencies. Image data in a plurality of frequency layers (only image data regarding a lowermost frequency layer is subjected to blending processing using image data regarding another frequency layer) obtained by hierarchically decomposing image data at a predetermined frame frequency is received. Image data for display is obtained according to a display capability, using image data regarding layers from the lowermost frequency layer up to a predetermined higher frequency layer. It is possible to reduce the motion picture quality degradation caused by the strobing in the layer of the basic frame frequency (frame frequency of the lowermost frequency layer) and to maintain the high image quality in the layers of the other frame frequencies.

A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, AR, parametric model of noise contained in the image, according to which a current noise pixel is computed as a combination of a linear combination of P previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective AR model linear combination parameters (φ.sub.1, . . . , φ.sub.P) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ), generating a noise template of noise pixels based on the estimated AR model parameters, wherein the noise template is of a predetermined pixel size smaller than the pixel size of the image, determining an estimate (σ.sub.P) of a variance of the noise template, and based on a comparison of the estimated variance (σ.sub.P) with a predetermined threshold (T.sub.σ), correcting the AR model variance parameter (σ).

In various examples, a deep neural network (DNN) based pre-filter for content streaming applications is used to dynamically adapt scene entropy (e.g., complexity) in response to changing network or system conditions of an end-user device. For example, where network and/or system performance issues or degradation are identified, the DNN may be implemented as a frame pre-filter to reduce the complexity or entropy of the frame prior to streaming—thereby allowing the frame to be streamed at a reduced bit rate without requiring a change in resolution. The DNN-based pre-filter may be tuned to maintain image detail along object, boundary, and/or surface edges such that scene navigation—such as by a user participating in an instance of an application—may be easier and more natural to the user.

Aspects of the disclosure provide methods and apparatuses for video processing. In some examples, an apparatus for video processing includes processing circuitry. The processing circuitry determines one or more parameters of a temporal filter based on video contents in an uncompressed video. The uncompressed video includes a sequence of frames. Then, the processing circuitry applies the temporal filter with the determined parameter on a first pixel in a first frame to determine a filtered value for the first pixel based on the first pixel in the first frame and second pixels in a group of reference frames for the first frame. Further, the processing circuitry encodes a filtered video that includes the filtered value for the first pixel in the first frame to generate a coded video bitstream that carries the filtered video.

Methods, systems and apparatus for video processing are described. One example video processing method includes performing a conversion between a video including a video region and a bitstream of the video according to a rule, wherein the rule specifies that an adaptive loop filtering operation is allowed for the video region in response to an absence of one or more adaptation parameter set (APS) network abstraction layer (NAL) units that include adaptive loop filtering data.

A mathematical model deriving apparatus includes an encoding unit that generates a plurality of deteriorated videos after encoding an original video, in accordance with a plurality of combinations of a plurality of encoding parameters for a codec setting, a quality estimation unit that calculates a quality estimation value of each of the plurality of deteriorated videos, and a deriving unit that outputs video quality in response to the plurality of encoding parameters as input and derives a coefficient of a mathematical model in accordance with the quality estimation value and the plurality of combinations of the plurality of encoding parameters. This allows for deriving a mathematical model capable of evaluating quality according to a codec setting.

An imaging device includes a modulator, a first grating pattern constituted by a plurality of lines, and a second grating pattern having a phase deviating from a phase of the first grating pattern, and modulates light intensity. The imaging device receives a first image signal output by the first grating pattern and a second image signal output by the second grating pattern, calculates difference data and a range of a difference between the first image signal and the second image signal, generates and sets a data conversion parameter at a regular interval from the difference data that is continuously input on the basis of the range of the difference and the difference data, generates compression image data by using the difference data and the data conversion parameter, compresses the generated compression image data, and includes information indicating the range of the difference into the compressed data.

The present disclosure relates to pre-processing of video images. In particular, the video images are pre-processed in an object-based manner, i.e., by applying different pre-processing to different objects detected in the image. Moreover, the pre-processing is applied to a group of images. As such, object detection is performed in a plurality of images and the pre-processing for the plurality of images may be adapted to the decoded images and is applied to the decoded images.

The present disclosure relates to pre-processing of video images. In particular, the video images are pre-processed in an object-based manner, i.e., by applying different pre-processing to different objects detected in the image. Moreover, the pre-processing is applied to a group of images. As such, object detection is performed in a plurality of images and the pre-processing for the plurality of images may be adapted to the decoded images and is applied to the decoded images.

An encoding device evaluates a plurality of processing and/or post-processing algorithms and/or methods to be applied to a video stream, and signals a selected method, algorithm, class or category of methods/algorithms either in an encoded bitstream or as side information related to the encoded bitstream. A decoding device or post-processor utilizes the signaled algorithm or selects an algorithm/method based on the signaled method or algorithm. The selection is based, for example, on availability of the algorithm/method at the decoder/post-processor and/or cost of implementation. The video stream may comprise, for example, downsampled multiplexed stereoscopic images and the selected algorithm may include any of upconversion and/or error correction techniques that contribute to a restoration of the downsampled images.