為了提高圖像編碼預(yù)測(cè)器的預(yù)測(cè)性能，提出了一種低復(fù)雜度，高效的自適應(yīng)預(yù)測(cè)方法。采用LMS(Least Mean Square)自適應(yīng)濾波技術(shù)進(jìn)行預(yù)測(cè)，并對(duì)預(yù)測(cè)值進(jìn)行減鄰域均值的改進(jìn)，有效克服了圖像的非零均值和非平穩(wěn)性特征，滿(mǎn)足LMS算法的要求，使預(yù)測(cè)性能得以提高 。通過(guò)對(duì)不同圖像的仿真結(jié)果表明，該方法的預(yù)測(cè)差值圖像的熵比GAP算法和MED算法的差值圖像的熵要小0.1 bit/piexl左右，均方誤差(MSE)也要小于后兩者的均方誤差。

　　Abstract:

　　A low complexity and efficient adaptive prediction method for image predictive coding was presented. The predictor employed LMS(Least Mean Square) adaptive filtering technology and improved predicted values by using local mean estimation substructure method，which overcame the non-zero and non?鄄stationaryof the image’s characteristics，met the requirements of LMS algorithm well and improved the capability of predicting. The simulation results of different images show that the entropy of the prediction error image using the presented method is less than the results of GAP algorithm and MED algorithm about 0.1 bit/pixel，and the Mean Square Error(MSE) of the prediction error image is also smaller than the latter two algorithms.