# US 9,436,705 B2 · Grading images and video clips > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 9,436,705 B2 - Document ID: US9436705B2 - Status: Granted - Kind: B2 - Application number: 14/231,611 - Filed: 2014-03-31 - Published: 2015-03-19 - Granted: 2016-09-06 - Earliest claimed: 2013-09-17 - Assignees: Google Technology Holdings LLC - Applicants: Google Technology Holdings LLC - Inventors: Babak Robert Shakib; Yuriy Musatenko; Oleksii Kasitskyi - Web page: https://patentgrep.com/patent/US9436705B2 ## CPC - G11B 27/034: on discs (G11B27/036, G11B27/038 take precedence) - G06F 3/04842: Selection of displayed objects or displayed text elements (G06F3/0482 takes precedence) - G06F 3/04845: for image manipulation, e.g. dragging, rotation, expansion or change of colour - G06F 3/0488: using a touch-screen or digitiser, e.g. input of commands through traced gestures - G06F 16/48: Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually - G06F 16/5838: {using colour} - G06F 16/5866: {using information manually generated, e.g. tags, keywords, comments, manually generated location and time information} - G06F 16/783: using metadata automatically derived from the content - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 11/60: Creating or editing images; Combining images with text - G06V 40/172: {Classification, e.g. identification} - G11B 27/02: Editing, e.g. varying the order of information signals recorded on, or reproduced from, record carriers - G11B 27/10: Indexing; Addressing; Timing or synchronising; Measuring tape travel - H04N 1/212: {Motion video recording combined with still video recording (television signal recording H04N5/76)} - G06F 3/04847: Interaction techniques to control parameter settings, e.g. interaction with sliders or dials - G06T 2200/24: involving graphical user interfaces [GUIs] - G06T 2200/32: involving image mosaicing - G11B: INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER (recording measured values in a way that does not require playback through a transducer G01D9/00; recording or playback apparatus using mechanically marked tape, e.g. punched paper tape, or using unit records, e.g. punched or magnetically marked cards G06K; transferring data from one type of record carrier to another G06K1/18; circuits for coupling output of reproducer to radio receiver H04B1/20; gramophone pick-ups or like acoustic electromechanical transducers or circuits therefor H04R) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING - H04N: PICTORIAL COMMUNICATION, e.g. TELEVISION ## Abstract Techniques apparatuses for grading images and video clips are described. These techniques and apparatuses enable highlights of these graded images and video clips to be more-quickly created, at a high quality, or using fewer resources. ## Claims ### Claim 1 A method comprising: receiving an image; grading the image based on two or more quality metrics, the two or more quality metrics including: one or more of: a brightness, a color variance, or an overall sharpness; and one or more of: an accumulated-faces area, a head orientation, a face or faces smiling, eye blinks, locations of one or more faces, an eye focal point, an aspect ratio, a uniqueness, a mistake, or a face blur; generating quality metric values for each quality metric; storing the quality metric values associated with the image; and using the quality metric values to generate a subset of graded images from a set of images that includes the image and other images, the other images having been graded based on the one or more quality metrics, the subset highlighting the set of the image and the other images. ### Claim 2 (depends on 1) The method as recited in claim 1, wherein the quality metrics used to grade the image include an independent metric that is independent of values for other instances of the independent metric associated with the other images. ### Claim 3 (depends on 1) The method as recited in claim 1, wherein the quality metrics used to grade the image include a relative metric, a quality metric value for the relative metric being based at least in part on other quality metric values for the relative metric associated with the other images. ### Claim 4 (depends on 1) The method as recited in claim 1, wherein the image is a frame of a video clip. ### Claim 5 (depends on 1) The method as recited in claim 1, wherein each of the quality metric values for the image is represented using a floating point value. ### Claim 6 (depends on 1) The method as recited in claim 1, wherein the image is captured by a mobile device and the grading is immediately responsive to the capture, or to two or more of: the capture; determination that the mobile device is drawing power from a non-battery power source; or determination that the mobile device is currently using low quantities of processing and memory resources. ### Claim 7 (depends on 1) The method as recited in claim 1, wherein the image is received from a mobile device over a communication network by a remote device and the grading is performed by the remote device. ### Claim 8 (depends on 1) The method as recited in claim 1, further comprising determining a contextual variety of the image and the other images, the contextual variety including one or more of an indoor scene, an outdoor scene, a nighttime scene, a daytime scene, or an association with particular events. ### Claim 9 (depends on 8) The method as recited in claim 8, further comprising receiving user input to weight the contextual variety of the image and the other images. ### Claim 10 (depends on 9) The method as recited in claim 9, further comprising determining associations for the image, the associations indicating an association with a person, a number of persons, a context, or an event, and wherein determining the subset is further based on the associations. ### Claim 11 A computing device comprising: a display; a processor; and a non-transitory computer-readable storage media device embodying computer-executable instructions that, when executed by the processor, perform operations comprising: receiving an image; grading the image based on two or more quality metrics, the two or more quality metrics including: one or more of: a brightness, a color variance, or an overall sharpness; and one or more of: an accumulated-faces area, a head orientation, a face or faces smiling, eye blinks, locations of one or more faces, an eye focal point, an aspect ratio, a uniqueness, a mistake, or a face blur; generating quality metric values for each quality metric; storing the quality metric values associated with the image; and using the quality metric values to generate a subset of graded images from a set of images that includes the image and other images, the other images having been graded based on the one or more quality metrics, the subset highlighting the set of the image and the other images. ### Claim 12 (depends on 11) The computing device of claim 11, further comprising presenting the subset of graded images on the display, the subset of graded images presented within a highlight reel. ### Claim 13 (depends on 11) The computing device of claim 11, wherein the quality metrics used to grade the image include an independent metric that is independent of values for other instances of the independent metric associated with the other images. ### Claim 14 (depends on 11) The computing device of claim 11, wherein the quality metrics used to grade the image include a relative metric, a quality metric value for the relative metric being based at least in part on other quality metric values for the relative metric associated with the other images. ### Claim 15 (depends on 11) The computing device of claim 11, wherein the image is a frame of a video clip. ### Claim 16 (depends on 11) The computing device of claim 11, wherein each of the quality metric values for the image is represented using a floating point value. ### Claim 17 (depends on 11) The computing device of claim 11, wherein the computing device is a mobile device, the image is captured by the mobile device, and the grading is: immediately responsive to the capture; or responsive to the capture and one of: a determination that the mobile device is drawing power from a non-battery power source or a determination that the mobile device is currently using low quantities of processing and memory resources. ### Claim 18 (depends on 11) The computing device of claim 11, wherein the image is received from a mobile device over a communication network by a remote device and the grading is performed by the remote device. ### Claim 19 (depends on 11) The computing device of claim 11, further comprising determining a contextual variety of the image and the other images, the contextual variety including one or more of an indoor scene, an outdoor scene, a nighttime scene, a daytime scene, or an association with particular events. ### Claim 20 (depends on 19) The computing device of claim 19, further comprising receiving user input to weight the contextual variety of the image and the other images, the weight affecting the subset generated from the set of images.