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The search area for a good macroblock match is decided by the ‘search parameter’, p, where p is the number of [[pixels]] on all four sides of the corresponding macro-block in the previous frame. The search parameter is a measure of motion. The larger the value of p, larger is the potential motion and the possibility for finding a good match. A full search of all potential blocks however is a computationally expensive task. Typical inputs are a macroblock of size 16 pixels and a search area of p = 7 pixels.
[[Block-matching and 3D filtering]] makes use of this approach to solve various [[image restoration]] [[inverse problems]] such as [[noise reduction]]<ref>{{cite journal |last1= Dabov |first1= Kostadin |last2= Foi |first2= Alessandro |first3= Vladimir |last3= Katkovnik |first4= Karen |last4= Egiazarian |date= 16 July 2007 |title= Image denoising by sparse 3D transform-___domain collaborative filtering |journal= IEEE Transactions on Image Processing |volume=16 |issue= 8 |pages= 2080–2095 |doi= 10.1109/TIP.2007.901238 |pmid= 17688213 |bibcode= 2007ITIP...16.2080D |citeseerx= 10.1.1.219.5398 }}</ref> and [[deblurring]]<ref>{{Cite journal|last1= Danielyan|first1= Aram|last2= Katkovnik|first2= Vladimir|last3= Egiazarian|first3= Karen|arxiv=1106.6180 |title= BM3D Frames and Variational Image Deblurring |journal= IEEE Transactions on Image Processing|volume= 21|issue= 4|pages=
== Motivation ==
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