Cross-layer cross-channel sample prediction

Patent metadata

Field	Value
Publication number	US-9860533-B2
Application number	US-201213977578-A
Country	US
Kind code	B2
Filing date	Jun 26, 2012
Priority date	Jun 26, 2012
Publication date	Jan 2, 2018
Grant date	Jan 2, 2018

Abstract

Systems, apparatus and methods are described including operations for video coding including cross-layer cross-channel sample prediction.

First claim

What is claimed:

1. A computer-implemented method for video coding, comprising:
reconstructing a reference sample for a reference channel in a reference layer of video data and a second reference sample for a second reference channel in the reference layer of video data;
determining a target prediction sample for a target channel in a target layer based at least in part on: only the reference sample via cross-layer cross-channel prediction in a some instances, only the second reference sample via cross-layer cross-channel prediction in other instances, and both the reference sample and the second reference sample via cross-layer cross-channel prediction in still further instances; and wherein the target channel is always a different channel than the reference channel and the target layer is always a different layer than the reference layer during cross-layer cross-channel prediction; and
coding the target channel based at least in part on the target prediction sample;
wherein when the reference layer comprises a base layer, the target layer comprises an enhancement layer;
wherein when the reference layer comprises an enhancement layer, the target layer comprises a higher enhancement layer
wherein when the reference channel comprises a luma channel, the target channel comprises a chroma channel; and
wherein when the reference channel comprises a chroma channel, the target channel comprises one of a luma channel or another chroma channel.

2. The method of claim 1 , wherein the determination of the target prediction sample is performed during scalable video coding for one or more scalability types including spatial scaling, temporal scaling, quality scaling, and bit-depth scaling.

3. The method of claim 1 , further comprising:
reconstructing a further reference sample for a further reference layer and/or for a further reference channel of the video data.

4. The method of claim 1 , further comprising:
reconstructing a further reference sample for a further reference layer and/or for a further reference channel of the video data,
wherein the determination of the target prediction sample for the target channel in the target layer is based at least in part on the further reference sample in addition to the reference sample, and
wherein the target layer is a higher layer than the further reference layer and/or the target channel is a different channel than the further reference channel.

5. The method of claim 1 , wherein the determination of the target prediction sample for the target channel in the target layer comprises selection of the reference layer and reference channel during decoding based at least in part on a flag associated with the target prediction sample during encoding.

6. The method of claim 1 , wherein determining the target prediction sample comprises applying one of a linear relation model or a non-linear relation model.

7. The method of claim 1 , further comprising:
receiving, via a decoder portion of a coder, parameter values from an encoder portion of the coder, wherein the parameter values are associated with performing cross-layer cross-channel prediction, and
wherein determining the target prediction sample comprises applying one of a linear relation model or a non-linear relation model based at least in part on the parameter values.

8. The method of claim 1 , further comprising:
determining, via a decoder portion of a coder, parameter values independent from and in parallel with an encoder portion of the coder, wherein the parameter values are associated with performing cross-layer cross-channel prediction, and
wherein determining the target prediction sample comprises applying one of a linear relation model or a non-linear relation model based at least in part on the parameter values.

9. The method of claim 1 , wherein model parameters are processed via one or more of the following operations: determining the target prediction sample by adaptively applying one of a one or more fixed relation model parameters, and adaptively determining one or more relation model parameters in response to model parameters associated with one or more layers and/or channels.

10. The method of claim 1 , wherein the determination of the target prediction sample for the target channel via cross-layer cross-channel prediction is adaptively applied based at least in part on a rate distortion cost.

11. The method of claim 1 , further comprising:
reconstructing a further reference sample for a further reference layer and/or for a further reference channel of the video data,
wherein the determination of the target prediction sample for the target channel in the target layer is based at least in part on the further reference sample in addition to the reference sample,
wherein the target layer is a higher layer than the further reference layer and/or the target channel is a different channel than the further reference channel,
wherein the determination of the target prediction sample is performed during scalable video coding for one or more scalability types including spatial scaling, temporal scaling, quality scaling, and bit-depth scaling,
wherein the determination of the target prediction sample for the target channel in the target layer comprises selection of the reference layer and reference channel during decoding based at least in part on a flag associated with the target prediction sample during encoding,
wherein parameter values are processed via one or more of the following operations: receiving, via a decoder portion of a coder, parameter values from an encoder portion of the coder, and determining, via a decoder portion of a coder, parameter values independent from and in parallel with an encoder portion of the coder, wherein the parameter values are associated with performing cross-layer cross-channel prediction,
wherein determining the target prediction sample comprises applying one of a linear relation model or a non-linear relation model based at least in part on the parameter values,
wherein model parameters are processed via one or more of the following operations: determining the target prediction sample by adaptively applying one of a one or more fixed relation model parameters, and adaptively determining one or more relation model parameters in response to model parameters associated with one or more layers and/or channels, and
wherein the determination of the target prediction sample for the target channel via cross-layer cross-channel prediction is adaptively applied based at least in part on a rate distortion cost.

12. A system for video coding on a computer, comprising:
a display device configured to present video data;
one or more processors communicatively coupled to the display device;
one or more memory stores communicatively coupled to the one or more processors;
a sample reconstruction logic module of the video coder communicatively coupled to the one or more processors and configured to reconstruct a reference sample for a reference channel in a reference layer of video data and a second reference sample for a second reference channel in the reference layer of video data; and
a cross-layer cross-channel prediction logic module of a video coder communicatively coupled to the sample reconstruction logic module and configured to determine a target prediction sample for a target channel in a target layer based at least in part on: only the reference sample via cross-layer cross-channel prediction in a some instances, only the second reference sample via cross-layer cross-channel prediction in other instances, and both the reference sample and the second reference sample via cross-layer cross-channel prediction in still further ins

Assignees

• Xu Lidong
• Han Yu
• Zhang Wenhao
• Chiu Yi-Jen
• Jiang Hong
• Intel Corp

Inventors

• Xu Lidong
• Han Yu
• Zhang Wenhao
• Chiu Yi-Jen
• Jiang Hong

Classifications

• H04N19/147

  according to rate distortion criteria (rate-distortion as a criterion for motion estimation H04N19/567) · CPC title

• H04N19/187

  the unit being a scalable video layer · CPC title

• H04N19/33

  in the spatial domain · CPC title

• H04N19/105

  Selection of the reference unit for prediction within a chosen coding or prediction mode, e.g. adaptive choice of position and number of pixels used for prediction · CPC title

• H04N19/573

  Motion compensation with multiple frame prediction using two or more reference frames in a given prediction direction · CPC title