Apple Inc.

David William Singer

Alexandros Michael Tourapis

Barin Geoffry Haskell

Atul Puri

Adriana Dumitras

Yeping Su

Hsi-Jung Wu

Fabrice A Robinet

Khaled Mammou

Jungsun Kim

Alagu S Periyannan

Anne Jones

Jay Geagan

Kevin L Gong

Krishnakanth Rapaka

Valery G Valentin

Guy Cote

Hye-Yeon Cheong

Christopher L Flick

Courtney Ann Kennedy

Dominik Mehlem

James O Normile

Haitao Guo

Sorin Constantin Cismas

Thomas Pun

Eric Olaf Carlson

Kevin Calhoun

Xiaosong Zhou

Athanasios Leontaris

Jim Chen Chou

Timothy John Millet

Dazhong Zhang

Steven E Saunders

John Samuel Bushell

Krasimir D Kolarov

Kenneth I Greenebaum

Xiaohua Yang

Jaehong Chon

Mengjou Lin

Alexandras Tourapis

Thal-Wey Then

John Kevin Calhoun

A system comprises an encoder configured to compress attribute information and/or spatial for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. Additionally, an encoder is configured to signal and/or a decoder is configured to receive a supplementary message comprising volumetric tiling information that maps portions of 2D image representations to objects in the point. In some embodiments, characteristics of the object may additionally be signaled using the supplementary message or additional supplementary messages.

Point cloud compression with supplemental information messages

A system comprises an encoder configured to compress attribute information and/or spatial information for a point cloud and/or a decoder configured to decompress compressed attribute and/or spatial information for the point cloud. To compress the attribute and/or spatial information, the encoder is configured to convert a point cloud into an image based representation. Also, the decoder is configured to generate a decompressed point cloud based on an image based representation of a point cloud. In some embodiments, a bit stream structure may be used to communicate compressed point cloud data. The bit stream structure may include point cloud compression network abstraction layer (PCCNAL) units that enable use of groups of frames (GOFs), frame, and sub-frame signaling of patch information. Such a bit stream structure may permit low delay streaming and random access reconstruction of point clouds amongst other applications.

Bit stream structure for compressed point cloud data

Techniques for encoding video with motion compensation include a compressed bitstream syntax that includes a list of all motion prediction reference frames without distinguishing between short-term reference frame and long-term reference frames. The list of reference frames may be provided in a slice header and may apply to encoded data video data within the corresponding slice. The list may be prefaced with a single number indicating the total number of reference frames. In an aspect delta POC reference numbers may be encoded with a flag indicating the sign of the delta POC when the absolute value of the POC is not equal to zero. In another aspect, a flag may be encoded for every reference frame indicating if POC information should be used when scaling prediction references, and a weighting parameter may be included when POC information should be used.

Reference picture management and list construction

Video compression and decompression techniques are disclosed that provide improved bandwidth control for video compression and decompression systems. In particular, video coding and decoding techniques quantize input video in multiple dimensions. According to these techniques, pixel residuals may be generated from a comparison of an array of input data to an array of prediction data. The pixel residuals may be quantized in a first dimension. After the quantization, the quantized pixel residuals may be transformed to an array of transform coefficients. The transform coefficients may be quantized in a second dimension and entropy coded. Decoding techniques invert these processes. In still other embodiments, multiple quantizers may be provided upstream of the transform stage, either in parallel or in cascade, which provide greater flexibility to video coders to quantize data in different dimensions in an effort to balance the competing interest in compression efficiency and quality of reconstructed video.

Multidimensional quantization techniques for video coding/decoding systems

A video encoding and decoding system that implements an adaptive transfer function method internally within the codec for signal representation. A focus dynamic range representing an effective dynamic range of the human visual system may be dynamically determined for each scene, sequence, frame, or region of input video. The video data may be cropped and quantized into the bit depth of the codec according to a transfer function for encoding within the codec. The transfer function may be the same as the transfer function of the input video data or may be a transfer function internal to the codec. The encoded video data may be decoded and expanded into the dynamic range of display(s). The adaptive transfer function method enables the codec to use fewer bits for the internal representation of the signal while still representing the entire dynamic range of the signal in output.

Adaptive transfer function for video encoding and decoding

A system comprises an encoder configured to compress video data using an in-loop noise generation process that generates noise in the compression loop at a sub-image portion level of granularity, such as at a block level. The encoder includes noise model and/or noise model input parameter information in an encoded bit stream. Also, a system includes a decoder configured to receive such a bit stream and decompress the video using an in-loop noise generation process that generates noise in the decompression loop at a sub-image portion level of granularity.

Video compression with in-loop sub-image level controllable noise generation

A system comprises an encoder configured to compress media objects using a compression loop that includes a residual decomposition component that decomposes a residual signal for a block of the media object being compressed into multiple sub-error signals. The encoder is further configured to enable different transformation and/or quantization processes to be specified to be applied to different ones of the sub-errors. A corresponding decoder is configured to apply inverse transformation/quantization processing to the sub-error signals, based on the transformation/quantization processes that were applied at the encoder. The decoder then re-creates a residual signal from the processed sub-error signals and uses the re-created residual signal to correct predicted values at the decoder.

Media object compression/decompression with adaptive processing for block-level sub-errors and/or decomposed block-level sub-errors

Predictive coding techniques may include resampling of reference pictures, where various coding parameters are determined based on the resolution(s) or pixel format(s) of the prediction references. In a first aspect, lists of weights for use in weighted prediction are based on the resolution(s) of prediction references. In a second aspect, resampling filter parameters are selected based on the resolutions of prediction references. In a third aspect, deblocking filter parameters are based on the resolution(s) of prediction references.

Reference picture re-sampling

Improved video coding and decoding techniques are described, including techniques to derive quantization step sizes adaptively with quantization step size table templates. Quantization techniques described provide finer-grained control over quantization with a more flexible quantization step size especially at higher degrees of quantization. This may result in improved overall compression quality. Other coding parameters, such as in-loop filtering parameters, may be derived based on the more flexible quantization parameters.

Adaptive quantizer design for video coding

A multi-stage coding method includes receiving an input block of data for encoding and one or more previously coded samples associated with the input block. The input block is segmented into at least a first sub-region and a second sub-region. A prediction for the first sub-region is generated based on the one or more previously coded samples. Residual data for the first sub-region is obtained using the prediction for the first sub-region. A reconstruction of the first sub-region is generated using the residual data for the first sub-region and the prediction for the first sub-region. A prediction for the second sub-region is generated using the reconstruction of the first sub-region. Residual data for the second sub-region is obtained using the prediction for the second sub-region. The input block is encoded based in part on the residual data for the first region and the residual data for the second region.

Multi-stage block coding

The present disclosure describes techniques for coding and decoding video in which a plurality of coding hypotheses are developed for an input pixel block of frame content. Each coding hypothesis may include generation of prediction data for the input pixel block according to a respective prediction search. The input pixel block may be coded with reference to a prediction block formed from prediction data derived according to plurality of hypotheses. Data of the coded pixel block may be transmitted to a decoder along with data identifying a number of the hypotheses used during the coding to a channel. At a decoder, an inverse process may be performed, which may include generation of a counterpart prediction block from prediction data derived according to the hypothesis identified with the coded pixel block data, then decoding of the coded pixel block according to the prediction data.

Techniques of multi-hypothesis motion compensation

Techniques for coding and decoding video may include predicting picture regions defined by a time-varying tessellation and/or by a tessellation that varies spatially within a picture. These techniques improve decoded video quality, for example, by reducing block-based visual artifacts. Tessellation patterns may be irregular spatially to prevent alignment of some prediction region boundaries within a picture. Tessellation patterns may vary over time based on a spatial offset value, and the spatial offset value may be determined via a modulo function. Tessellation patterns may include overlapped shapes, for example when used in conjunction with overlapped block motion compensation.

Flexible block partitioning structures for image/video compression and processing

A method of adaptive chroma downsampling is presented. The method comprises converting a source image to a converted image in an output color format, applying a plurality of downsample filters to the converted image and estimating a distortion for each filter chose the filter that produces the minimum distortion. The distortion estimation includes applying an upsample filter, and a pixel is output based on the chosen filter. Methods for closed loop conversions are also presented.

Adaptive chroma downsampling and color space conversion techniques

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San Francisco, CA, United States of America

David William Singer