Indian Institute of Technology Hyderabad (IITH) is a premier institute of science and technology established in 2008. IITH has been consistently ranked in the&nbsp;<a href="https://iith.ac.in/reports/" rel="noopener noreferrer">top 10 institutes</a>&nbsp;in India for Engineering&nbsp;<a href="https://www.nirfindia.org/2021/EngineeringRanking.html" rel="noopener noreferrer" target="_blank">according to NIRF</a>&nbsp;making it one of the most coveted schools for science and technology in the country.

IITH offers&nbsp;<a href="https://iith.ac.in/academics/under-graduate/">undergraduate programs</a>&nbsp;in all the classical engineering disciplines, applied sciences, design, as well as several modern interdisciplinary areas. Students are given a flexibility to explore a broad set of areas, and potentially pursue a minor or double major in a discipline that is not their own. Students who wish to seek a deeper understanding of their own discipline are strongly encouraged to get involved in cutting-edge research with the help of a faculty to mentor them, and earn an Honors in their own field.

The very foundation of IIT Hyderabad is based on&nbsp;<a href="https://iith.ac.in/research/">research and innovation</a>. The vibrant research culture is evident from the number of patents and publications that IITH produces consistently every year. IITH offers&nbsp;<a href="https://iith.ac.in/academics/programmes-offered/">graduate programs</a>&nbsp;at both a masters, and a doctoral level, in several diverse areas. There are separate programs for technology, design, science, and liberal arts. The&nbsp;<a href="https://iith.ac.in/academics/post-graduate/">MTech program</a>&nbsp;is offered in various modes and durations to cater to the ever-growing need of postgraduate level professionals in the country.

IITH encourages and supports&nbsp;<a href="https://iic-iith.netlify.app/index.html" rel="noopener noreferrer" target="_blank">innovation and entrepreneurship</a>&nbsp;at every stage. The minor program in entrepreneurship is open to all students of IITH, and many of the courses are offered by industrialists who are entrepreneurs themselves. There are various&nbsp;<a href="https://iith.ac.in/research/centres-incubators/">innovation centers and incubators</a>&nbsp;that promote entrepreneurship and help mentor young innovators. IITH has been very successful in building tie-ups with leading academic institutions around the globe.

IITH enjoys a very special relationship with Japanese universities and industries that goes beyond academic and research collaborations. In fact, some of the iconic buildings in IITH campus will carry the signature of Japanese architecture.

IITH creates a unique holistic ecosystem for education that offers interactive learning, a very flexible academic structure, cutting-edge research, strong industry collaboration, and entrepreneurship. This is an environment which enables students and faculty to translate their dreams into realities.

IIT Hyderabad

We show that the structural similarity (SSIM) index, which is used in image processing to assess the similarity between an image representation and an original reference image, can be formulated as a locally quadratic distortion measure. We, furthermore, show that recent results of Linder and Zamir on the rate-distortion function (RDF) under locally quadratic distortion measures are applicable to this SSIM distortion measure. We finally derive the high-resolution SSIM-RDF and provide a simple method to numerically compute an approximation of the SSIM-RDF of real images. © 2011 Jan stergaard et al.

World University root node

Department of Electrical Engineering

Eurasip Journal on Advances in Signal Processing

The high-resolution rate-distortion function under the structural similarity index

In this paper, we derive bounds on the structural similarity (SSIM) index as a function of quantization rate for fixed-rate uniform quantization of image discrete cosine transform (DCT) coefficients under the high-rate assumption. The space domain SSIM index is first expressed in terms of the DCT coefficients of the space domain vectors. The transform domain SSIM index is then used to derive bounds on the average SSIM index as a function of quantization rate for uniform, Gaussian, and Laplacian sources. As an illustrative example, uniform quantization of the DCT coefficients of natural images is considered. We show that the SSIM index between the reference and quantized images fall within the bounds for a large set of natural images. Further, we show using a simple example that the proposed bounds could be very useful for rate allocation problems in practical image and video coding applications. © 2008 IEEE.

Fulltext

IEEE Transactions on Image Processing

Rate bounds on SSIM index of quantized images

We propose an algorithm for designing linear equalizers that maximize the structural similarity (SSIM) index between the reference and restored signals. The SSIM index has enjoyed considerable application in the evaluation of image processing algorithms. Algorithms, however, have not been designed yet to explicitly optimize for this measure. The design of such an algorithm is nontrivial due to the nonconvex nature of the distortion measure. In this paper, we reformulate the nonconvex problem as a quasi-convex optimization problem, which admits a tractable solution. We compute the optimal solution in near closed form, with complexity of the resulting algorithm comparable to complexity of the linear minimum mean squared error (MMSE) solution, independent of the number of filter taps. To demonstrate the usefulness of the proposed algorithm, it is applied to restore images that have been blurred and corrupted with additive white gaussian noise. As a special case, we consider blur-free image denoising. In each case, its performance is compared to a locally adaptive linear MSE-optimal filter. We show that the images denoised and restored using the SSIM-optimal filter have higher SSIM index, and superior perceptual quality than those restored using the MSE-optimal adaptive linear filter. Through these results, we demonstrate that a) designing image processing algorithms, and, in particular, denoising and restoration-type algorithms, can yield significant gains over existing (in particular, linear MMSE-based) algorithms by optimizing them for perceptual distortion measures, and b) these gains may be obtained without significant increase in the computational complexity of the algorithm. © 2008 IEEE.

Design of linear equalizers optimized for the structural similarity index

Automated analysis of fluorescence microscopy images of endothelial cells labeled for actin is important for quantifying changes in the actin cytoskeleton. The current manual approach is laborious and inefficient. The goal of our work is to develop automated image analysis methods, thereby increasing cell analysis throughput. In this study, we present preliminary results on comparing different algorithms for cell segmentation and image denoising.

AMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium

Comparison of pre-processing techniques for fluorescence microscopy images of cells labeled for actin.

In this paper, we present a novel algorithm for wavelet domain image denoising using the soft thresholding function. The thresholds are designed to be locally optimal with respect to the structural similarity (SSIM) index. The SSIM Index is first expressed in terms of wavelet transform coefficients of orthogonal wavelet transforms. The wavelet domain representation of the SSIM Index, along with the assumption of a Gaussian prior for the wavelet coefficients is used to formulate the soft thresholding optimization problem. A locally optimal solution is found using a quasi-Newton approach. This solution is applied to denoise images in the wavelet domain. The visual quality of the images denoised using the proposed algorithm is shown to be higher compared to the MSE-optimal soft thresholding denoising solution, as measured by the SSIM Index. © 2008 IEEE.

Proceedings - International Conference on Image Processing, ICIP

Perceptual soft thresholding using the structural similarity index

In this paper, we present an algorithm for designing a linear equalizer that is optimal with respect to the structural similarity (SSIM) index. The optimization problem is shown to be non-convex, thereby making it non-trivial. The non-convex problem is first converted to a quasi-convex problem and then solved using a combination of first order necessary conditions and bisection search. To demonstrate the usefulness of this solution, it is applied to image denoising and image restoration examples. We show using these examples that optimizing equalizers for the SSIM index does indeed result in higher perceptual image quality compared to equalizers optimized for the ubiquitous mean squared error (MSE). ©2008 IEEE.

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SSIM-Optimal linear image restoration

In this paper, we derive bounds on the Structural SIMilarity (SSIM) Index as a function of quantization rate for fixed-rate uniform quantization of image discrete cosine transform (DCT) coefficients under the high rate assumption. The space domain SSIM Index is first expressed in terms of the DCT coefficients of the space domain vectors. The transform domain SSIM Index is then used to derive bounds on the average SSIM Index as a function of quantization rate for Gaussian and Laplacian sources. As an illustrative example, uniform quantization of the DCT coefficients of natural images is considered. We show that the SSIM Index between the reference and quantized images fall within the bounds for a large set of natural images. Further, we show using a simple example that the proposed bounds could be very useful for rate allocation problems in practical image and video coding applications. © 2008 IEEE.

Data Compression Conference Proceedings

Rate bounds on SSIM index of quantized image DCT coefficients

We use a perceptual distortion metric - the structural similarity (SSIM) index, to derive a new linear estimator for estimating zero-mean Gaussian sources distorted by additive white Gaussian noise (AWGN). We use this estimator in an image denoising application and compare its performance with the traditional linear least squared error (LLSE) estimator. Although images denoised using the SSIM-optimized estimator have a lower peak signal-to-noise ratio (PSNR) compared to their LLSE couterparts, the SSIM-optimized estimator clearly outperforms the LLSE estimator in terms of the visual quality of the denoised images. ©2006 IEEE.

A linear estimator optimized for the structural similarity index and its application to image denoising

Multiple description codes generated by quantized frame expansions have been shown to perform well on erasure channels when compared to traditional channel codes. In this paper we propose a multiple description image coding scheme in the wavelet domain using quantized frame expansions. We form zerotrees from wavelet coefficients and apply a tight frame operator to the zerotrees. We then group appropriate expansions to form packets and evaluate the performance of the scheme over an erasure channel. We compare the performance of the proposed scheme with a conventional channel coding scheme. © 2005 IEEE.

Frame based multiple description image coding in the wavelet domain

The statistics of natural scenes in the wavelet domain are accurately characterized by the Gaussian Scale Mixture (GSM) model. The model lends itself easily to analysis and many applications that use this model are emerging (for e.g., denoising, watermark detection). In this paper, we present an error-resilient image communications application that uses the GSM model and Multiple description coding (MDC) to provide error-resilience. We derive a rate-distortion bound for GSM random variables, derive the redundancy rate distortion function, and finally implement an MD image communication system. © 2005 IEEE.

Multiple description image coding using natural scene statistics

This paper presents a multiple description image coding scheme that facilitates the transmission of digital imagery over multiple noisy channels. The proposed scheme divides the image into smaller parts that are transmitted over the individual channels of an inverse multiplexing system. The division or splitting is done in such as fashion that if facilities the interpolation of lost coefficients in the case of one or more channel failures. At the reciever, the image is reconstructed by proper assembly of the data received from each channel. In case of channel failure, the missing coefficients are estimated from the available data with the use of a novel post-processing scheme. For operation over four noisy channels with various bit error probabilites, we investigate the quantitative and subjective performance of the proposed system for the case of multiple channel failures.

Coding of digital imagery for transmission over multiple noisy channels

This paper presents a wavelet-based image coder for transmission over the binary symmetric channel (BSC). The proposed coder achieves error resilience by using a joint source-channel coding strategy. The coder uses trellis-coded quantization (TCQ) for source coding, and punctured convolutional (PC) codes for channel coding. The total available bit rate is partitioned into source coding rate and channel coding rate, so as to achieve optimal performance at the given BER. © 2001 IEEE.

ISCAS 2001 - 2001 IEEE International Symposium on Circuits and Systems, Conference Proceedings

Joint source-channel coding of images using punctured convolutional codes and trellis-coded quantization

This paper presents a multiple description image coding scheme that facilitates the transmission of digital imagery over multiple noisy channels. The proposed scheme divides the image into smaller parts that are transmitted over the individual channels of an inverse multiplexing system. The division or splitting is done in such a fashion that it facilitates the interpolation of lost coefficients in the case of one or more channel failures. To combat the effects of channel noise, a novel joint source-channel coding scheme is employed, which uses punctured convolutional channel codes and trellis-coded quantization. At the receiver, the image is reconstructed by proper assembly of the data received from each channel. In case of channel failure, the missing coefficients are estimated from the available data with the use of a novel post processing scheme. For operation over four noisy channels with various bit error probabilities, we investigate the quantitative and subjective performance with and without channel failures.