# US 2014/0193094 A1 · Edge smoothing block filtering and blending > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2014/0193094 A1 - Document ID: US20140193094A1 - Status: Published application - Kind: A1 - Application number: 13/761,268 - Filed: 2013-02-07 - Published: 2014-07-10 - Earliest claimed: 2013-01-10 - Assignees: Broadcom Corporation - Applicants: Broadcom Corporation - Inventors: Jaewon Shin; Brian Schoner - Web page: https://patentgrep.com/patent/US20140193094A1 ## CPC - G06T 5/70: Denoising; Smoothing - G06T 5/20: using local operators - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract Aspects of an edge smoothing block filter and combinations of the filter with a motion compensated temporal filter are described. In one embodiment, edge smoothing block filtering includes selecting a current pixel to be filtered and selecting a candidate pixel within a search area about the current pixel. The edge smoothing block filtering generally seeks to identify candidate pixels having surrounding pixels similar to pixels surrounding the current pixel. The edge smoothing block filtering further computes a cost difference between pixels within a candidate pixel neighborhood and pixels within a current pixel neighborhood, and filters the current pixel based on the cost difference. Aspects of the filters and filter element combinations may preserve edges and textures adaptively based on image content. For example, diagonal or curved image edges may be filtered along edges while texture is preserved along the edges. ## Claims ### Claim 1 A method of edge smoothing block filtering, comprising: selecting a current pixel as a pixel to be filtered; selecting a candidate pixel within a search area about the current pixel; computing a cost difference between pixels within a candidate pixel neighborhood about the candidate pixel and pixels within a current pixel neighborhood about the current pixel; converting the cost difference to a similarity measure based on a smoothing noise threshold; and filtering the current pixel based on the candidate pixel and the similarity measure. ### Claim 2 (depends on 1) The method of claim 1, wherein: converting the cost difference to a similarity measure further comprises converting the cost difference to the similarity measure based on a weight function, the weight function being selected according to a measure of filter strength; and the method further comprises setting a value of the smoothing noise threshold according to a function of the cost difference. ### Claim 3 (depends on 2) The method of claim 2, wherein filtering the current pixel comprises filtering the current pixel based on a value of the candidate pixel weighted by the similarly measure. ### Claim 4 (depends on 1) The method of claim 1, wherein computing a cost difference comprises, for each of a plurality of candidate pixels within the search area about the current pixel, computing a cost difference between pixels within a candidate pixel neighborhood about the candidate pixel and the pixels within the current pixel neighborhood about the current pixel. ### Claim 5 (depends on 4) The method of claim 4, further comprising: setting a value of the smoothing noise threshold based on a function of the cost differences of the plurality of candidate pixels, wherein converting the cost difference to a similarity measure further comprises converting each of the cost differences to a similarity measure based on the smoothing noise threshold. ### Claim 6 (depends on 5) The method of claim 5, wherein setting a value of the smoothing noise threshold further comprises setting the value of the smoothing noise threshold according to one of a fixed threshold value or a fraction of a maximum value of the cost differences. ### Claim 7 (depends on 6) The method of claim 6, further comprising: when the fraction of the maximum value of the cost differences is less than the fixed threshold value, setting the value of the smoothing noise threshold to the fixed threshold value; and when the fraction of the maximum value of the cost differences is greater than or equal to the fixed threshold value, setting the value of the smoothing noise threshold to the fraction of the maximum value of the cost differences. ### Claim 8 (depends on 7) The method of claim 7, further comprising setting the fraction of the fraction of the maximum value of the cost differences according to a desired amount of smoothing. ### Claim 9 (depends on 8) The method of claim 8, further comprising normalizing each of the similarity measures. ### Claim 10 (depends on 9) The method of claim 9, wherein filtering the current pixel comprises filtering the current pixel based on the value of each of the plurality of candidate pixels weighted by the normalized similarly measures. ### Claim 11 An edge smoothing block filter, comprising: a current pixel selector that selects a current pixel as a pixel to be filtered; a candidate pixel selector that selects a candidate pixel within a search area about the current pixel; a neighborhood calculator that computes a cost difference between pixels within a candidate pixel neighborhood about the candidate pixel and pixels within a current pixel neighborhood about the current pixel; a weighter that converts the cost difference to a similarity measure based on a weight function and a smoothing noise threshold, the weight function being selected according to a measure of filter strength; and a current pixel updater that filters the current pixel based on a value of the candidate pixel weighted by the similarly measure. ### Claim 13 (depends on 11) The edge smoothing block filter of claim 11, wherein the neighborhood calculator further computes, for each of a plurality of candidate pixels within the search area about the current pixel, a cost difference between pixels within a candidate pixel neighborhood about the candidate pixel and the pixels within the current pixel neighborhood about the current pixel. ### Claim 14 (depends on 13) The edge smoothing block filter of claim 13, wherein the weighter sets a value of the smoothing noise threshold based on a function of the cost differences of the plurality of candidate pixels. ### Claim 15 (depends on 14) The edge smoothing block filter of claim 14, wherein the weighter sets the value of the smoothing noise threshold according to one of a fixed threshold value or a fraction of a maximum value of the cost differences. ### Claim 16 A method of edge smoothing block filtering and blending, comprising: receiving an input signal comprising image data; edge smoothing block filtering the input signal to generate an edge smoothed signal; selecting one of the input signal and the edge smoothed signal as a multiplexed output signal based on a motion blend input signal; motion compensation blending the multiplexed output signal with a motion compensated signal, to generate a blended motion compensated signal; and output blending at least one of the input signal, the blended motion compensated signal, and the edge smoothed signal based on an output blend mode signal. ### Claim 17 (depends on 16) The method of claim 16, wherein output blending comprises outputting at least one of the blended motion compensated signal, the edge smoothed signal, or a blend of the blended motion compensated signal and the edge smoothed signal based on an output blend input signal. ### Claim 18 (depends on 17) A method of claim 17, wherein the edge smoothing block filtering further comprises: selecting a candidate pixel within a search area about a current pixel to be filtered; computing a cost difference between pixels within a candidate pixel neighborhood about the candidate pixel and pixels within a current pixel neighborhood about the current pixel; converting the cost difference to a similarity measure based on a smoothing noise threshold; and filtering the current pixel based on the candidate pixel and the similarity measure. ### Claim 19 (depends on 18) The method of claim 18, further comprising setting a value of the smoothing noise threshold according to a function of the cost difference. ### Claim 20 (depends on 19) The method of claim 19, further comprising: when a fraction of the cost difference is less than a fixed threshold value, setting the value of the smoothing noise threshold to the fixed threshold value; and when the fraction of the cost difference is greater than or equal to the fixed threshold value, setting the value of the smoothing noise threshold to the fraction of the cost difference.