An Effective Alternative Structural Similarity Index Algorithm (original) (raw)

An Improvement of Structural Similarity Index for Image Quality Assessment

Journal of Computer Science, 2014

The image quality assessment has been widely used in image processing. Several researches have been developed and carried considering the Human Visual System (HVS). Under the hypothesis that human visual perception is extremely adapted to retrieve structural information from a scene, the SSIM index is the most widely used in this area, which leads to a better correlation with HVS. Despite its robustness the SSIM presents some limitations in the presence of blur affecting images. In this study, we propose an improved version of the SSIM for blur image assessment. The idea is to combine gradient based SSIM score with that of the structural information of the blur. Experimental results show a good performance.

Structural Similarity Based Image Quality Assessment Using Full Reference Method

This paper presents an objective quality assessment for digital images that have been degraded by noise. Objective quality assessment is crucial and is generally used in image processing. The main objective of this paper is to analyse various statistical properties and their measurements and finally compare them. The statistical properties that are included are mean square error (MSE), root mean square error (RMSE), signal to noise ratio (SNRQ), peak signal to noise ratio (PSNR) and certain frequency parameters like spectral magnitude distortions and spectral phase distortions. But it is observed that MSE and PSNR yield poor results therefore a new metric namely structure similarity is proposed which has a better performance than MSE and PSNR but fails when applied on badly blurred images. Therefore, edge based structure similarity index metric (ESSIM) is proposed. Experiment results show that ESSIM is more consistent with human visual system (HVS) than SSIM and PSNR especially for the blurred images.

Structural similarity weighting for image quality assessment

2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW), 2013

Recently, there has been a trend of investigating weighting/pooling strategies in the research of image quality assessment (IQA). The saliency maps, information content maps and other weighting strategies were reportedly to be able to amend performance of IQA metrics to a sizable margin. In this work, we will show that local structural similarity is itself an effective yet simple weighting scheme leading to substantial performance improvement of IQA. More specifically, we propose a Structural similarity Weighted SSIM (SW-SSIM) metric by locally weighting the SSIM map with local structural similarities computed using SSIM itself. Experimental results on LIVE database confirm the performance of SW-SSIM as compared to some major weighting/pooling type of IQA methods, such as MS-SSIM, WSSIM and IW-SSIM. We would like to emphasize that our SW-SSIM is merely a straightforward realization of a more general framework of locally weighting IQA metric using itself as similarity measures.

Contrast Weighted Perceptual Structural Similarity Index for Image Quality Assessment

2009

In this paper a full reference objective image quality assessment technique is presented which is based on the properties of the human visual system (HVS). By integrating the notion of perceptually important regions with the measurement of structural similarity between the original image and distorted image a contrast weighted Perceptual Structural SIMilarity Index PSSIMc is proposed. The method first evaluates the structural similarity indices between the original and distorted image in local regions. These local indices are then weighted based on the perceptual weights of the corresponding region, characterized by the contrast value of the local region. PSSIMc of an image is calculated as the average of these weighted indices. A comparison with the peak-signal-to-noise ratio (PSNR) and state of the art metric, Mean Structural Similarity Index (MSSIM), shows that the proposed measure correlates better with the judgment of human observers.

Multiscale structural similarity for image quality assessment

The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, 2003

The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multi-scale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method.

GPU Based Image Quality Assessment using Structural Similarity SSIM Index

This chapter deals with performance analysis of CUDA implementation of an image quality assessment tool based on structural similarity index (SSI). Since it had been initial created at the University of Texas in 2002, the Structural SIMilarity (SSIM) image assessment algorithm has become a valuable tool for still image and video processing analysis. SSIM provided a big giant over MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) techniques because it way more closely aligned with the results that would have been obtained with subjective testing. For objective image analysis, this new technique represents as significant advancement over SSIM as the advancement that SSIM provided over PSNR. The method is computationally intensive and this poses issues in places wherever real time quality assessment is desired. We tend to develop a CUDA implementation of this technique that offers a speedup of approximately 30 X on Nvidia GTX275 and 80 X on C2050 over Intel single core processor.

Image Quality Assessment with Structural Similarity Using Wavelet Families at Various Decompositions

Smart Innovation, Systems and Technologies, 2015

Wavelet transform is one of the most active areas of research in the image processing. This paper gives analysis of a very well known objective image quality metric, so called Structural similarity, MSE and PSNR which measures visual quality between two images. This paper presents the joint scheme of wavelet transform with structural similarity for evaluating the quality of image automatically. In the first part of algorithm, each distorted as well as original image are decomposed into three levels and in second part, these coefficient are used to calculate the structural similarity index, MSE and PSNR. The predictive performance of image quality based on the wavelet families like db5, haar (db1), coif1 with one, two and three level of decomposition is figured out. The algorithm performance includes the correlation measurement like Pearson, Kendall, and Spearman correlation between the objective evaluations with subjective one.