DOI: 10.36347/sjet

Pages: 613-620

Many Video compression algorithms manipulate video frames to dramatically reduce the storage requirements and bandwidth required for transmission while maximizing perceived video quality. Typical video compression methods first transform the video frames from its spatial domain representation to frequency domain representation using some transform technique such as Discrete Cosine Transform vector quantization, fractal compression, and Discrete Wavelet Transform and then code the transformed values. Recently, instead of performing a frequency transformation, machine learning based approach has been proposed which has two fundamental steps: selecting the most representative pixels and colorization. Our proposed method converts the color video frames to gray scale frames and the color information for only a few representative pixels is stored. At the decoder side is all the color values for the gray scale pixels across frames is predicted. Selecting the most representative pixels is essentially an active learning problem, while colorization is a semi-supervised learning problem. In this paper, we propose a novel active learning method for automatically extracting the RP is proposed for video compression. In this paper the active learning problem is formulated into an RP minimization problem resulting in the optimal RP set in the sense that it minimizes the error between the original and the reconstructed color frame.