613 Scholars Journal of Engineering and Technology (SJET) ISSN 2321-435X (Online) Sch. J. Eng. Tech., 2014; 2(4C):613-620 ISSN 2347-9523 (Print) ©Scholars Academic and Scientific Publisher (An International Publisher for Academic and Scientific Resources) www.saspublisher.com Research Article A Video Compression Technique Based On Active Learning Approach Shireen Fathima* 1 , Mohammed Azharuddin Ahmed 2 1 PG-Student, HKBK College of engineering, Bangalore, India 2 Technical trainer, RIIIT, Mysore, India *Corresponding author Shireen Fathima Email: Abstract: 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. Keywords: video compression; active learning; semi-supervised learning; representative pixels. INTRODUCTION With the evolution of internet and WWW, there was a need to transmit images, videos and other multimedia objects over the network and for this various compression techniques were proposed to achieve better throughput. Some of these techniques focused on high compression ratio while others on better quality and appreciable compression ratio. Video compression is a crucial technique for reducing the bandwidth required to transmit videos. Video data contains spatial and temporal redundancy. Similarities can thus be encoded by merely registering differences within a frame (spatial), and between frames (temporal).Recently, machine learning based approach has been proposed for video compression [1-2]. Instead of performing a frequency transformation, Cheng et al. proposed to convert the color video to a gray scale video. A few representative pixels are selected whose color information is stored. The gray scale video and the selected color pixels are used to learn a statistical model to predict the color values for the rest of the pixels[3-5]. Their empirical result has shown that good compression ratio can be achieved while the image quality is reasonably good according to Peak Signal to Noise Ratio (PSNR) score. From a machine learning perspective, there are two fundamental problems. First, how to select the most representative pixels, which is essentially an active learning problem. The selected pixels, together with the gray scale video, are stored as the encoding process. Second, how to combine color and gray scale information of the pixels to learn a model, which is essentially a semi-supervised learning problem[6]. The learned model is used to recover the color video as the decoding process. In this paper, we propose to new active and semi-supervised learning for video compression which does not requires iteration. RELATED WORK Cheng et al. developed a straightforward active learning algorithm which iteratively selects the pixels on which the prediction errors are the highest. The major disadvantage of Cheng's approach is that there is no theoretical guarantee that the predication error can actually be reduced by using the selected pixels[1]. LapRLS algorithm: The use of LapRLS for video compression is based on the assumption that if two pixels have similar intensity values and are spatially close to each other then it is very likely that they have similar color values[7]. Consider z to denote the labeled point, and x to denote any point (either labeled or unlabeled).