01668nas a2200217   4500000000100000000000100001008004100002260001200043653002700055653001600082653002500098653004400123100001600167700001600183245006600199856008100265300001200346490000600358520107200364022001401436       2021                            d  c06/202110aB-spline Curve Fitting10aCompression10aLeast Square Fitting10aProgressive and Iterative Approximation1 aM. J. Ebadi1 aA. Ebrahimi00aVideo Data Compression by Progressive Iterative Approximation  uhttps://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_19.pdf  a189-1950 v63 aIn the present paper, the B-spline curve is used for reducing the entropy of video data. We consider the color or luminance variations of a spatial position in a series of frames as input data points in Euclidean space R or R3. The progressive and iterative approximation (PIA) method is a direct and intuitive way of generating curve series of high and higher fitting accuracy. The video data points are approximated using progressive and iterative approximation for least square (LSPIA) fitting. The Lossless video data compression is done through storing the B-spline curve control points (CPs) and the difference between fitted and original video data. The proposed method is applied to two classes of synthetically produced and naturally recorded video sequences and makes a reduction in the entropy of both. However, this reduction is higher for syntactically created than those naturally produced. The comparative analysis of experiments on a variety of video sequences suggests that the entropy of output video data is much less than that of input video data.  a1989-1660