IOSR Journal of Engineering (IOSRJEN) www.iosrjen.org ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 05 (May. 2014), ||V5|| PP 23-27 International organization of Scientific Research 23 | P a g e Structural Similarity Based Efficient Multi-View Video Coding L.C.Manikandan 1 , M.A.Anusha 2 , Dr. A. Lenin Fred 3 1 (Asst.Professor) Department of Computer Science and Engineering, Mar Ephraem College of Engineering and Technology, Marthandan, Tamilnadu, India. 2 (PG Student) Department of Computer Science and Engineering, Mar Ephraem College of Engineering and Technology, Marthandam, Tamilnadu, India. 3 (Professor) Department of Computer Science and Engineering, Mar Ephraem College of Engineering and Technology, Marthandam, Tamilnadu, India. Abstract: - Efficient compression of multi-view images and video is crucial for 3DV application. The major task of three-dimensional video (3DV) coding is to provide high quality depth data. For experimental coding purposes, the compression method used is H.264/AVC video coding standard. In this paper, Structural similarity (SSIM) based Rate distortion (RD) optimization is expressed for distortion metric. In multi-view video, the left view and the right view have their pixels with strong dependencies of structural information that are spatially close. The full reference quality metric SSIM is used to exploit the structural information from different views in multi-view video coding. With the proposed system, significant gains can be achieved in terms of SSIM index. Keywords: - DCT, MB, Multi-view video coding, SSIM, Rate distortion optimization I. INTRODUCTION Today’s advances in display and camera technology enable new applications for three-dimensional video (3DV) [1], [2] and have become one of the promising fields regarding the development of new application for natural scenes. The 3-D video proffers high quality multimedia experience to the consumer through display, signal and transmission technologies. The display and receiver side of 3-D video may gain an increased level in the production of 3-D content [3]. For the 3DV, the display technology may need multi-views and for that, at least two views are necessary. With the High efficiency video coding (HEVC) based multi-view compression scheme [4], forward compatibility with HEVC is guaranteed similarly to the forward compatibility provided by Multi-view video coding (MVC). The MVC was enabling a certain view to be inter-view predicted from an earlier decoded view. The encoder can choose a Rate Distortion (RD) optimal way in the interview prediction. Additional to this, the complexity restriction limiting inter-view prediction is only for within the same time instance. In the proposed system, the full reference quality metric SSIM is used to exploit the structural information from different views in multi-view video coding. The rest of this paper is organized as follows. Section II gives [5], [2] an introduction to multi-view video coding and the compression techniques for multiple views. Section III will show an introduction to structural similarity, Section IV [4], [6], [7] will discuss the proposed changes with structural similarity to enable multi-view video coding. II. MULTIVIEW VIDEO CODING The multiple video cameras are used to simultaneously acquire various view points of the scene. The resulting data are often referred to as multi-view video [5]. As the multi-view video imagery captures the same 3-D scene from different viewpoints, there exist inter-view statistical dependencies among the images [2]. The similarities between the images can be classified into Inter-view similarity and Temporal similarity, 2.1 Inter-view similarity Objects in each image are subject to parallax and appear at different pixel locations. To exploit this inter-view similarity, disparity compensation techniques are used. 2.2 Temporal similarity Objects appear in successive images but at different pixel locations. To exploit these temporal similarities, motion compensation techniques have been developed. III. STRUCTURAL SIMILARITY The structural similarity (SSIM) index is a method for measuring the similarity between two images/videos. The SSIM index is a full reference metric, the measuring of image quality based on an initial uncompressed or distortion-free image as reference. SSIM is designed to improve on traditional methods