Structural Similarity Estimation Research Articles

ESTIMATION OF STRUCTURAL SIMILARITY FOR CONTROL OF MICROELECTRONIC PRODUCTS

In this paper we consider the technological process of manufacturing micromechanical gyroscopes, the output of which is divided into cells of the crystal plate and each cell contains a fixed number of product elements. The estimation of structural similarity for measurements of technical characteristics of various devices is offered. A relatively large value of structural similarity assessment means that there is a dependence of the quality of the manufactured devices on the position on the crystal plate. This gives a good reason for further work to improve the quality of the process or to change the technological scheme as a whole. The article considers a numerical example based on a real sample of microelectronic products. The proposed method can be used both in the process of adjustment of the production process and in the rejection of products, when further configuration of the equipment is no longer possible.

Blind structural similarity estimation of digital images using quantized discrete cosine transform coefficients

Objective image quality assessment is used to develop a quantitative measure in order to predict perceived image quality by exploiting a variety of known properties of the human visual system (HVS). A new paradigm for quality assessment of the image is based on the structural similarity (SSIM) index, which takes advantage of characteristics of the HVS. In order to estimate SSIM, we need to know the source image to quantify the visibility of errors between the distorted image and the referenced image. In many practical applications, however, the reference image is not available and a blind quality assessment should be utilized. In this paper, an algorithm for statistical estimation of the SSIM based on the probability density functions (pdfs) of quantized discrete cosine transform (DCT) coefficients is presented. In the proposed method, we assume that the pdfs of the original DCT coefficients follow a specific distribution. The parameters of this distribution are then obtained from the quantization step size and quantized DCT coefficients of the distorted image, which are then used to calculate the SSIM metric. Our proposed method is, therefore, applicable to the encoding schemes that involve DCT quantization such as JPEG encoding. Numerical results show that the proposed SSIM estimation method provides relative errors that are generally smaller than those of the available peak signal-to-noise ratio estimation schemes for DCT-based images.

Reduced-Reference Image Quality Assessment by Structural Similarity Estimation

Reduced-reference image quality assessment (RR-IQA) provides a practical solution for automatic image quality evaluations in various applications where only partial information about the original reference image is accessible. Here we propose an RR-IQA method by estimating the structural similarity (SSIM) index, which is a widely used full-reference (FR) image quality measure shown to be a good indicator of perceptual image quality. Specifically, we extract statistical features from a multi-scale, multi-orientation divisive normalization transform and develop a distortion measure by following the philosophy in the construction of SSIM. We found an interesting linear relationship between the FR SSIM measure and our RR estimate when the image distortion type is fixed. A regression-bydiscretization method is then applied to normalize our measure across image distortion types. We use six publiclyavailable subject-rated databases to test the proposed RR-SSIM method, which shows strong correlations with both SSIM and subjective quality evaluations. Finally, we introduce the novel idea of partially repairing an image using RR features and use deblurring as an example to demonstrate its application.

SSIM-Motivated Rate-Distortion Optimization for Video Coding

We propose a rate-distortion optimization (RDO) scheme based on the structural similarity (SSIM) index, which was found to be a better indicator of perceived image quality than mean-squared error, but has not been fully exploited in the context of image and video coding. At the frame level, an adaptive Lagrange multiplier selection method is proposed based on a novel reduced-reference statistical SSIM estimation algorithm and a rate model that combines the side information with the entropy of the transformed residuals. At the macroblock level, the Lagrange multiplier is further adjusted based on an information theoretical approach that takes into account both the motion information content and perceptual uncertainty of visual speed perception. Finally, the mode for H.264/AVC coding is selected by the SSIM index and the adjusted Lagrange multiplier. Extensive experiments show that the proposed scheme can achieve significantly better rate-SSIM performance and provide better visual quality than conventional RDO coding schemes.