# US 2021/0344940 A1 · Method for image processing and apparatus for implementing the same

> Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt

- Number: US 2021/0344940 A1
- Document ID: US20210344940A1
- Status: Published application
- Kind: A1
- Application number: 17/307,364
- Filed: 2021-05-04
- Published: 2021-11-04
- Earliest claimed: 2020-05-04
- Assignees: Ateme
- Applicants: Ateme
- Inventors: Thomas GUIONNET; Sébastien HERBRETEAU
- Web page: https://patentgrep.com/patent/US20210344940A1

## CPC

- H04N 19/46: Embedding additional information in the video signal during the compression process (H04N19/517, H04N19/68, H04N19/70 take precedence)
- H04N 19/85: using pre-processing or post-processing specially adapted for video compression
- G06T 5/70: Denoising; Smoothing
- H04N 5/262: Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects {; Cameras specially adapted for the electronic generation of special effects}
- H04N 19/117: Filters, e.g. for pre-processing or post-processing (sub-band filter banks H04N19/635)
- H04N 19/136: Incoming video signal characteristics or properties
- H04N 19/177: the unit being a group of pictures [GOP]
- H04N 19/179: the unit being a scene or a shot
- H04N 19/182: the unit being a pixel
- H04N 19/36: Scalability techniques involving formatting the layers as a function of picture distortion after decoding, e.g. signal-to-noise [SNR] scalability
- H04N 19/80: Details of filtering operations specially adapted for video compression, e.g. for pixel interpolation (H04N19/635, H04N19/86 take precedence)
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL

## Abstract

A method of method of processing an image includes: determining estimates of parameters of an auto-regressive, parametric model of noise, according to which a current noise pixel is computed combining linear combination of previous noise pixels in a causal neighborhood of the current noise pixel weighted by respective model linear combination parameters with a generated noise sample corresponding to an additive Gaussian noise of model variance parameter; performing a convergence check loop, each iteration including: generating a noise template of noise pixels based on the estimated model parameters, the noise template having predetermined pixel size smaller than the image pixel size; estimating a noise template variance; if the estimated variance is below a first predetermined threshold or above a second predetermined threshold, proportionally decreasing the model linear combination parameters with a predetermined correcting factor, and performing another convergence check loop; otherwise exiting the convergence check loop.

## Claims

### Claim 1

A method of processing an image, the method comprising:
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 (φ1, . . . , φP) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ);
performing a convergence check loop, wherein each iteration of the convergence check loop comprises:
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 (σP) of a variance of the noise template;
if the estimated variance (σP) is below a first predetermined threshold (Tmin) or above a second predetermined threshold (Tmax), proportionally decreasing one or more of the AR model linear combination parameters (φ1, . . . , φP) with a predetermined correcting factor, and performing a new iteration of the convergence check loop;
otherwise exiting the convergence check loop.

### Claim 2 (depends on 1)

The method according to claim 1, wherein the AR model is configured to model grain contained in the image.

### Claim 3 (depends on 1)

The method according to claim 1, performed at an encoder configured for encoding the image, wherein the predetermined pixel size of the noise template is chosen corresponding to the pixel size of a noise synthesis template used at a decoder to synthesize film grain based on the AR model.

### Claim 4 (depends on 1)

The method according to claim 1, wherein the predetermined pixel size of the noise template is chosen to be 64×64 pixels.

### Claim 5 (depends on 1)

The method according to claim 1, wherein proportionally decreasing the AR model linear combination parameters (φ1, . . . , φP) with the predetermined correcting factor comprises dividing each of the AR model linear combination parameters (φ1, . . . , φP) by the predetermined correcting factor, wherein the predetermined correcting factor is greater than 1.

### Claim 6 (depends on 1)

The method according to claim 1, wherein the predetermined correcting factor is equal to 1.4.

### Claim 7 (depends on 1)

The method according to claim 1, wherein the first predetermined threshold (Tmin) is defined based on the additive Gaussian noise of AR model variance parameter (σ).

### Claim 8 (depends on 1)

The method according to claim 1, wherein the second predetermined threshold (Tmax) is defined based on the additive Gaussian noise of AR model variance parameter (σ).

### Claim 9 (depends on 1)

The method according to claim 1, wherein the first predetermined threshold (Tmin) is defined as comprising the additive Gaussian noise of AR model variance parameter (σ) divided by a first predetermined scaling factor (Kmin).

### Claim 10 (depends on 1)

The method according to claim 1, wherein the second predetermined threshold (Tmax) is defined as comprising the additive Gaussian noise of AR model variance parameter (σ) multiplied by a second predetermined scaling factor (Kmax).

### Claim 11 (depends on 1)

The method according to claim 1, wherein at least one of the AR model linear combination parameters (φ1, . . . , φP) is preset to zero.

### Claim 12 (depends on 11)

The method according to claim 11, wherein a number of AR model linear combination parameters that are preset to zero is chosen based on a pixel resolution of the image.

### Claim 13

An apparatus, the apparatus comprising a processor and a memory operatively coupled to the processor, wherein the processor is configured to perform a method of processing an image, the method comprising:
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 (φ1, . . . , φP) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ);
performing a convergence check loop, wherein each iteration of the convergence check loop comprises:
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 (φP) of a variance of the noise template;
if the estimated variance (φP) is below a first predetermined threshold (Tmin) or above a second predetermined threshold (Tmax), proportionally decreasing one or more of the AR model linear combination parameters (φ1, . . . , φP) with a predetermined correcting factor, and performing a new iteration of the convergence check loop;
otherwise exiting the convergence check loop.

### Claim 14 (depends on 13)

A video encoder, configured to encode video content comprising a plurality of images, the video encoder comprising an apparatus according to claim 13 configured to process images of an input video.

### Claim 15

(canceled)

### Claim 16

A non-transitory computer-readable medium encoded with executable instructions which, when executed, causes an apparatus comprising a processor operatively coupled with a memory, to perform a method of processing an image, the method comprising:
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 (φ1, . . . , φP) with a generated noise sample corresponding to an additive Gaussian noise of AR model variance parameter (σ);
performing a convergence check loop, wherein each iteration of the convergence check loop comprises:
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 (φP) of a variance of the noise template;
if the estimated variance (φP) is below a first predetermined threshold (Tmin) or above a second predetermined threshold (Tmax), proportionally decreasing one or more of the AR model linear combination parameters (φ1, . . . , φP) with a predetermined correcting factor, and performing a new iteration of the convergence check loop;
otherwise exiting the convergence check loop.

### Claim 17 (depends on 13)

The apparatus according to claim 13, wherein the AR model is configured to model grain contained in the image.

### Claim 18 (depends on 13)

The apparatus according to claim 13, wherein the first predetermined threshold (Tmin) is defined based on the additive Gaussian noise of AR model variance parameter (σ).

### Claim 19 (depends on 13)

The apparatus according to claim 13, wherein the second predetermined threshold (Tmax) is defined as comprising the additive Gaussian noise of AR model variance parameter (σ) multiplied by a second predetermined scaling factor (Kmax).

### Claim 20 (depends on 16)

The non-transitory computer-readable medium according to claim 16, wherein the AR model is configured to model grain contained in the image.

### Claim 21 (depends on 16)

The non-transitory computer-readable medium according to claim 16, wherein the first predetermined threshold (Tmin) is defined based on the additive Gaussian noise of AR model variance parameter (σ).