# US 12,764,417 B2 · Real time assessment of picture quality

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- Number: US 12,764,417 B2
- Document ID: US12764417B2
- Status: Granted
- Kind: B2
- Application number: 19/021,306
- Filed: 2025-01-15
- Published: 2025-05-15
- Granted: 2026-10-13
- Earliest claimed: 2012-10-23
- Assignees: Snapaid Ltd.
- Applicants: SnapAid Ltd.
- Inventors: Ishay Sivan
- Web page: https://patentgrep.com/patent/US12764417B2

## CPC

- H04N 23/64: {Computer-aided capture of images, e.g. transfer from script file into camera, check of taken image quality, advice or proposal for image composition or decision on when to take image}
- G06T 7/0002: {Inspection of images, e.g. flaw detection}
- G06T 7/97: {Determining parameters from multiple pictures (depth or shape recovery from multiple images G06T7/55; stereo camera calibration G06T7/85)}
- G06V 20/64: Three-dimensional [3D] objects
- H04N 23/67: Focus control based on electronic image sensor signals
- H04N 23/6812: {based on additional sensors, e.g. acceleration sensors}
- H04N 23/80: Camera processing pipelines; Components thereof
- H04N 23/951: by using two or more images to influence resolution, frame rate or aspect ratio
- G06T 2207/10016: Video; Image sequence
- G06T 2207/10032: Satellite or aerial image; Remote sensing
- G06T 2207/30168: Image quality inspection
- H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION
- G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING

## Abstract

A computerized method for computing the photo quality of a captured image in a device image acquisition system, comprising on-board combining of a plurality of quality indicators computed from said captured image and its previous image frames quality indicators and a confidence level for at least one of said quality indicators, and using a processor to determine, based on said combining, whether photo quality is acceptable and taking differential action depending on whether quality is or is not acceptable.

## Claims

### Claim 1

A method for presenting a suggestion to a user of a device to move the device to a different location, for use with the device that comprises at least one digital camera module that comprises at least one optical lens and an image sensor coupled to the optical lens for capturing an image, and at least one processor coupled to the image sensor or digital camera for receiving data therefrom, the method by the processor comprising:
calculating from the received image by at least one sensor and lens, a quality indicator QI1 of a camera focus or a quality indicator of blur detection or of both;
calulating an auesthetic quality indicator QI2 that uses a background bluring test of a subject;
calculating a total quality indicator that is based at least partially on at least one of QI1 and QI2;
selecting, based on the total quality indicator, at least one appropriate suggestion from a pre-stored table of suggestions, suggesting to the user to move the device to different location; and
presenting the suggestion to the user.

### Claim 2 (depends on 1)

The method according to claim 1, where the background blurring test is based at least partially on data from at least one of, the sensor, the optical lens, a lens aperture or any combination thereof.

### Claim 3 (depends on 1)

The method according to claim 1, where the total quality indicator also comprises of testing an obstruction of at least one lens.

### Claim 4 (depends on 2)

The method according to claim 2, where a new quality indicator QI3 of at least one of the focus distance or the lens aperture are used in determining a depth of field of the image, where the depth of field is computed, based on a movement of the device in z axis, where the z axis is the direction to the subject in scene; and the quality indicator QI3 may be included in the total quality indicator.

### Claim 5 (depends on 2)

The method according to claim 2, where a separate QI2 is calculated for each lens and the device has, where QI2 of QI_total or both are based on at least two QI1 from 2 such lenses and sensor modules.

### Claim 6 (depends on 2)

The method according to claim 2, where a separate QI1i is calculated for each lens and sensor the device has, where QI1 of QI_total or both are based on at least two QI1i from 2 such lenses and sensor modules.

### Claim 7 (depends on 6)

The method according to claim 6, where a confidence level of focus is calculated based on a correlation between each QI2, and the confidence level is used in computation of QI_total.

### Claim 8 (depends on 7)

The method according to claim 7, where the total quality indicator also comprises of testing an obstruction of at least one lens.

### Claim 9 (depends on 1)

The method according to claim 1, where the total quality indicator also comprises of testing an obstruction of at least one lens.

### Claim 10 (depends on 1)

The method according to claim 1, further comprising building at least a partial reconstruction of a 3D scene according to the images from the camera module; wherein the suggestion is further based on the 3D scene reconstruction.

### Claim 11 (depends on 1)

The method according to claim 1, wherein the background bluring test uses algorithms of a deep learning.

### Claim 12 (depends on 1)

The method according to claim 1, wherein calculating quality indicator QI1 uses algorithms of a deep learning.