# US 2013/0156342 A1 · No-Reference Spatial Aliasing Measure for Digital Image Resizing > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2013/0156342 A1 - Document ID: US20130156342A1 - Status: Published application - Kind: A1 - Application number: 13/770,486 - Filed: 2013-02-19 - Published: 2013-06-20 - Earliest claimed: 2009-10-09 - Assignees: AT&T Intellectual Property I, L.P. - Inventors: Amy Ruth Reibman - Web page: https://patentgrep.com/patent/US20130156342A1 ## CPC - G06T 5/70: Denoising; Smoothing - G06T 7/00: Image analysis - G06T 7/0002: {Inspection of images, e.g. flaw detection} - G06T 2207/20052: Discrete cosine transform [DCT] - G06T 2207/20182: Noise reduction or smoothing in the temporal domain; Spatio-temporal filtering - G06T 2207/30168: Image quality inspection - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A method includes identifying an area in an image that is associated with strong directional energy. The method further includes determining a first estimate of signal energy for the area and a second estimate of aliasing energy for the area. The method further includes estimating a severity of an aliasing error of the image based on the first estimate and the second estimate. ## Claims ### Claim 1 A method comprising: identifying an area in an image, wherein the area is associated with strong directional energy; determining a first estimate of signal energy for the area and a second estimate of aliasing energy for the area; and estimating a severity of an aliasing error of the image based on the first estimate. ### Claim 2 (depends on 1) The method of claim 1, further comprising determining a quantitized direction based on the strong directional energy associated with the area. ### Claim 3 (depends on 2) The method of claim 2, further comprising determining on aliasing location based on the quantitized direction. ### Claim 4 (depends on 3) The method of claim 3, wherein the first estimate of the second estimate is determined based on the aliasing location. ### Claim 5 (depends on 1) The method of claim 1, wherein the area is identified based at least in part on the area having a particular contrast variance. ### Claim 6 (depends on 1) The method of claim 1, further comprising measuring jagginess in the image caused by aliasing. ### Claim 7 (depends on 1) The method of claim 1, wherein estimating the severity of the aliasing error includes determining a signal-to-aliasing ratio. ### Claim 8 (depends on 1) The method of claim 1, wherein the image is converted from a second image, the second image being different in size than the image. ### Claim 9 A method comprising: identifying a plurality of areas in an image, the plurality of areas associated with strong directional energy, wherein the strong direction energy is determined based on a particular amount of contrast variance; selecting a particular area of the plurality of areas, based at least in part on a particular contrast variance; and determining a patch image based on selecting a particular set of the plurality of areas in the image that are associated with the strong directional energy, wherein the particular set excludes the particular area, and wherein the particular set includes at least one of the plurality of areas. ### Claim 10 (depends on 9) The method of claim 9, further comprising removing image boundary effects of the image from the patch image by application of a Blackman-Harris window function. ### Claim 11 (depends on 9) The method of claim 9, further comprising removing image boundary effects of the image from the patch image by subtracting a mean brightness value of the image from data associated with each of the plurality of areas. ### Claim 12 (depends on 9) The method of claim 9, wherein each area of the particular set of the plurality of areas is located no more than thirty-two pixels from the particular area. ### Claim 13 (depends on 9) The method of claim 9, wherein the particular set of the plurality of areas includes twenty percent of the plurality of areas. ### Claim 14 (depends on 9) The method of claim 9, wherein the particular set of the plurality of areas includes twenty percent of the plurality of areas that are overlapping. ### Claim 15 (depends on 9) The method of claim 9, wherein the particular area is an area that has a largest contrast variance of the plurality of areas. ### Claim 16 A method comprising: identifying a plurality of areas in an image, the plurality of areas associated with strong directional energy, wherein the strong direction energy is determined based on a particular amount of contrast variance; reducing boundary effects of the image based at least in part on application of a Blackman-Harris window function to each of the plurality of areas; applying one or more directional masks based on Bresenham lines to one or more directions of the strong directional energy to identify one or more particular areas of the plurality of areas; and applying an aliasing mask to the one or more particular areas. ### Claim 17 (depends on 16) The method of claim 16, wherein the one or more directional masks indicate one or more frequency groups of the strong directional energy. ### Claim 18 (depends on 16) The method of claim 16, wherein the Blackman-Harris window function is applied to a center portion of each of the plurality of areas. ### Claim 19 (depends on 16) The method of claim 16, further comprising subtracting a mean brightness value of the image from data associated with each of the plurality of areas. ### Claim 20 (depends on 16) The method of claim 16, further comprises folding the one or more directional masks to form the aliasing mask.