R.Preethi Int. Journal of Engineering Research and Applications www.ijera.com ISSN: 2248-9622, Vol. 6, Issue 1, (Part - 4) January 2016, pp.127-133 www.ijera.com 127|Page A Novel Approaches For Chromatic Squander Less Visceral Coding Techniques Using Maneuver Stabilization R.Preethi, S.Igni Sabasti Prabu PG.Student .Dept.of IT Sathayabama University, Chennai, India Asst. Proffessor Dept of IT, Sathayabama University, Chennai, India Abstract Recent advances in video capturing and display technologies, along with the exponentially increasing demand of video services, challenge the video coding research community to design new algorithms able to significantly improve the compression performance of the current H.264/AVC standard. This target is currently gaining evidence with the standardization activities in the High Efficiency Video Coding (HEVC) project. The distortion models used in HEVC are mean squared error (MSE) and sum of absolute difference (SAD). However, they are widely criticized for not correlating well with perceptual image quality. The structural similarity (SSIM) index has been found to be a good indicator of perceived image quality. Meanwhile, it is computationally simple compared with other state-of-the-art perceptual quality measures and has a number of desirable mathematical properties for optimization tasks. We propose a perceptual video coding method to improve upon the current HEVC based on an SSIM-inspired divisive normalization scheme as an attempt to transform the DCT domain frame prediction residuals to a perceptually uniform space before encoding. Based on the residual divisive normalization process, we define a distortion model for mode selection and show that such a divisive normalization strategy largely simplifies the subsequent perceptual rate-distortion optimization procedure. We further adjust the divisive normalization factors based on local content of the video frame. Experiments show that the scheme can achieve significant gain in terms of rate-SSIM performance and better visual quality when compared with HEVC Index Terms— SSIM index, Normalization factor, perceptual video coding, rate distortion optimization, residual divisive normalization, H.264/AVC coding I. INTRODUCTION The main objective of video coding is to optimize the perceptual quality of the reconstructed video within available bit rate. Ideally, the distortion model used in the video coding framework should correlate perfectly with perceived distortion of the Human Visual System (HVS), which is the ultimate consumer of the video content. However, almost all existing video coding techniques use the Sum of Absolute Difference (SAD) or Sum of Square Difference (SSD) as the distortion model. It has been widely criticized in the literature that SAD and SSD measures correlate poorly with the HVS [1]. Fortunately, a lot of research has been done recently towards perceptual image quality assessment (IQA) models that perform significantly better than SSD or SAD in predicting perceptual image quality. Among them, the structural similarity (SSIM) index [1] is widely used in quantifying compression artifacts because of its accuracy, simplicity and efficiency. Recently, there have been a number of efforts to design video coding techniques based on the SSIM index, e.g., mode selection [2] and rate control [3]. Sum of Absolute difference is algorithm to measure the similarity. Absolute difference between each pixel in the original block and corresponding pixel in the block being used for comparison. This will create block similarity. The main purpose of object recognition is that it will identify even small part of image can be identified. E.g. Template Search Image 2 5 5 2 7 5 8 6 4 0 7 1 7 4 2 7 7 5 9 8 4 6 8 5 Left Center Right 0 2 0 5 0 3 3 3 1 3 7 3 3 4 5 0 2 0 1 1 3 3 1 1 1 3 4 The main advantage of object recognition is that searching for object inside the image lighting, color, direction, size, and shape can be identified and Edge detection is so reliability of result. Since the HVS has varying sensitivity to different frequencies, frequency weighting [4] has been incorporated in the quantization process in RESEARCH ARTICLE OPEN ACCESS