# US 2020/0288129 A1 · Method and device for encoding/decoding video signal by using optimized conversion based on multiple graph-based model

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- Number: US 2020/0288129 A1
- Document ID: US20200288129A1
- Status: Published application
- Kind: A1
- Application number: 15/932,206
- Filed: 2016-08-19
- Published: 2020-09-10
- Earliest claimed: 2015-08-19
- Assignees: LG Electronics Inc.
- Applicants: LG Electronics Inc.
- Inventors: Hilmi E. EGILMEZ; Jana Ehmann; Onur G. GULERYUZ
- Web page: https://patentgrep.com/patent/US20200288129A1

## CPC

- H04N 19/12: Selection from among a plurality of transforms or standards, e.g. selection between discrete cosine transform [DCT] and sub-band transform or selection between H.263 and H.264
- H04N 19/159: Prediction type, e.g. intra-frame, inter-frame or bidirectional frame prediction
- H04N 19/18: the unit being a set of transform coefficients
- H04N 19/61: in combination with predictive coding
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION

## Abstract

The present invention, with respect to a method of processing video data, provides a method of processing video data, provides a method characterized by comprising the steps of: performing a clustering for the video data; generating at least one data cluster as a result of the clustering; generating at least one Graph laplacian matrix corresponding to the at least one data cluster; performing conversion optimization on the basis of multiple graph-based models, wherein the multiple graph-based models respectively include at least one graph laplacian matrix; and generating an optimized conversion matrix according to the results of performing the conversion optimization.

## Claims

### Claim 1

A method for processing video data, the method comprising:
performing a clustering on the video data;
generating at least one data cluster as a result of the clustering;
generating at least one graph laplacian matrix corresponding to the at least one data cluster;
performing a transform optimization based on multiple graph-based models, each of the multiple graph-based models including at least one graph laplacian matrix; and
generating an optimized transform matrix according to a result of the transform optimization.

### Claim 2 (depends on 1)

The method of claim 1, wherein
the clustering is performed based on a prediction mode.

### Claim 3 (depends on 2)

The method of claim 2, wherein
when the prediction mode is an intra-prediction mode, the at least one data cluster represents intra-residual data for the intra-prediction mode.

### Claim 4 (depends on 3)

The method of claim 3, wherein
each of the multiple graph-based models is generated for the intra-prediction mode.

### Claim 5 (depends on 2)

The method of claim 2, wherein
when the prediction mode is an inter-prediction mode, the at least one data cluster represents inter-residual data for the inter-prediction mode.

### Claim 6 (depends on 1)

The method of claim 1, wherein
the transform optimization is performed based on a specified metric, and the specified metric minimizes a squared sum of off-diagonal elements.

### Claim 7 (depends on 6)

The method of claim 6, wherein the transform optimization includes:
initializing the transform matrix;
selecting two basic vectors of the transform matrix; and
updating the two basic vectors based on the specified metric.

### Claim 8

A device for processing video data, the device comprising:
a clustering unit configured to perform a clustering on the video data to generate at least one data cluster;
a graph signal modeling unit configured to generate a graph laplacian matrix corresponding to the at least one data cluster; and
a transform optimizing unit configured to perform transform optimization based on multiple graph-based models and generate an optimized transform matrix,
wherein each of the multiple graph-based models includes at least one graph-based laplacian.

### Claim 9 (depends on 8)

The device of claim 8, wherein
the clustering is performed based on a prediction mode.

### Claim 10 (depends on 9)

The device of claim 9, wherein
when the prediction mode is an intra-prediction mode, the at least one data cluster represents intra-residual data for the intra-prediction mode.

### Claim 11 (depends on 10)

The device of claim 10, wherein
each of the multiple graph-based models is generated for the intra-prediction mode.

### Claim 12 (depends on 9)

The device of claim 9, wherein
when the prediction mode is an inter-prediction mode, the at least one data cluster represents inter-residual data for the inter-prediction mode.

### Claim 13 (depends on 8)

The device of claim 8, wherein
the transform optimization is performed based on a specified metric, and the specified metric minimizes a squared sum of off-diagonal elements.

### Claim 14 (depends on 13)

The device of claim 13, wherein the transform optimizing unit includes:
a transform initializing unit configured to initialize the transform matrix;
a transform selecting unit configured to select two basic vectors of the transform matrix; and
a transform updating unit configured to update the two basic vectors based on a specified metric.