Abstract—Real-time image quality assessment algorithms is an important, outcome research is dedicated to improving this practice. Towards this end, a design of real-time implementable full-reference image or video quality algorithms that are based on the Structural Similarity (SSIM) index and multi-scale SSIM (MS-SSIM) index preferred. The proposed algorithms merged into one single updating process. LIVE image quality database used to evaluate their improvement in form of computational complexity. Experimental results show that the proposed algorithm is an effective alternative for real-time image Structural Similarity with low area cost (time). Index Terms—Real time, Structural Similarity, effective. I. INTRODUCTION Image quality assessment is an emerging field of signal processing. More or less defined as the task of designing an algorithm to automatically judge the perceived “quality” of a photograph, it remains a largely open problem. Latest trends indicate beginning of a new era in digital images and videos, digitized visual information. In addition to the increasing amount of available digital visual data, other factors make the problem of information extraction particularly complicated. First, users ask for more information to be extracted from their datasets, which requires increasingly complicated algorithms. Second, in many cases, the analysis needs to be done in real-time to reap the actual benefits. For instance, a security expert would strive for real-time analysis of the streaming video and audio data in conjunction. Managing and performing run-time analysis on such datasets is appearing to be the next big challenge in computing. Video quality evaluation is performed to describe the quality of a set of video sequences under study. Video quality can be evaluated objectively by mathematical models or subjectively by asking users for their rating. Also, the quality of a system can be determined offline (i.e., in a laboratory setting for developing new codec’s or services), or in-service to monitor Manuscript received July 9, 2015. This work was supported in part by the Malaysian. UTHM under Grant Vot 1301. Khairulnizam Othman is with the Embedded Computing Research Cluster Microelectronics and Nanotechnology – Shamsuddin Research Centre (MiNT-SRC), University Tun Hussein Onn Malaysia, Johor, Malaysia . Afandi Ahmad is with the Embedded Computing Research Cluster Microelectronics and Nanotechnology – Shamsuddin Research Centre (MiNT-SRC), University Tun Hussein Onn Malaysia, Johor, Malaysia . and ensure a certain level of quality. [1], while Full Reference Methods (FR) is FR metrics computes the quality difference by comparing the original video signal against the received video signal. Typically, every pixel from the source is compared against the corresponding pixel at the received video, with no knowledge about the encoding or transmission process in between. More elaborate algorithms may choose to combine the pixel-based estimation with other approaches such as described below. FR metrics are usually the most accurate at the expense of higher computational effort. The structural similarity (SSIM) index is a method for measuring the similarity between two images. The SSIM index is a full reference metric; in other words, the measuring of image quality based on an initial uncompressed or distortion-free image as reference. SSIM is designed to improve on traditional methods like peak signal-to-noise ratio (PSNR) and mean squared error (MSE), which have proven to be inconsistent with human eye perception. The difference with respect to other techniques mentioned previously such as MSE or PSNR is that these approaches estimate perceived errors; on the other hand, SSIM considers image degradation as perceived change in structural information. Structural information is the idea that the pixels have strong inter-dependencies especially when they are spatially close. These dependencies carry important information about the structure of the objects in the visual scene. A research topic that has attracted a great deal of attention in the past decade is to design novel objective image similarity or dissimilarity measures that correlate well with perceptual image fidelity or distortion [2]. The Structural Similarity (SSIM) index is widely used algorithm in FR image quality assessment applications. A number of algorithms have been derived from SSIM: Multi-scale SSIM (MS-SSIM), Percentile Pooling SSIM (PSSIM) [3], Complex-Wavelet SSIM index (CW-SSIM) [5], Gradient-based Structural Similarity (G-SSIM) [6], and Three-Component Weighted SSIM [7]. All these derivative algorithms aim to improve the accuracy but inevitably increase the computational complexity. II. PRELIMINARY A. Single Scale Structural Similarity Index Based on the trade-offs that the Human Visual system (HVS) is highly adapted for extracting structural information, the SSIM algorithm assesses three terms between two An Effective Alternative Structural Similarity Index Algorithm Khairulnizam Othman and Afandi Ahmad 2015 Int'l Conference on Intelligent Computing, Electronics Systems and Information Technology (ICESIT-15) Aug 25-26, 2015 Kuala Lumpur (Malaysia) http://dx.doi.org/10.15242/IAE.IAE0815017 44