Combining Active and Semi-Supervised Learning for Video Compression
    Download PDF
Chiyuan Zhang,Ming Ji. Combining Active and Semi-Supervised Learning for Video Compression. International Journal of Software and Informatics, 2013,7(3):453~468
Hits: 1301
Download times: 1176
Abstract:Video compression algorithms manipulate video signals to dramatically reduce the storage and bandwidth required while maximizing perceived video quality. Typical video compression methods include discrete cosine transform, vector quantization, fractal compression, and discrete wavelet transform. Recently, a machine learning based approach has been proposed which converts the color images (frames) to gray scale images (frames) and the color information for only a few representative pixels is kept. A learning model is then trained to predict the color values for the gray scale pixels across frames. Selecting the most representative pixels is essentially an active learning problem, while colorization is a semi-supervised learning problem. In this paper, we propose to combine active and semi-supervised learning for video compression. The basic idea is to minimize the size of the covariance matrix of the regularized least squares estimates, in which the regression model assumes that each pixel can be reconstructed by the other pixels with similar spatial location and intensity value. The experimental results demonstrate the effectiveness of the proposed approach for video compression.
keywords:video compression  active learning  semi-supervised learning
View Full Text  View/Add Comment  Download reader

 

 

more>>  
Visitor:2145005
Top Paper  |  FAQ  |  Guest Editors  |  Email Alert  |  Links  |  Copyright  |  Contact Us

© Copyright by Institute of Software, the Chinese Academy of Sciences
京ICP备05046678号-5

京公网安备 11040202500065号