Reduced-reference SSIM estimation

Rehman

IEEE Trans. Circuits Syst. Video Technol.

et al. 2012

Self Cite

193

Abstract-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.Index Terms-H.264/AVC coding, Lagrange multiplier, rate-distortion optimization, reduced-reference image quality assessment, structural similarity (SSIM) index.

Section: A Rr Ssim Modelmentioning

confidence: 99%

SSIM-Motivated Rate-Distortion Optimization for Video Coding

Rehman

IEEE Trans. Circuits Syst. Video Technol.

et al. 2012

Self Cite

193

“…It has also been found to be powerful in modeling the neuronal responses in the visual cortex [36,37]. Divisive normalization has been successfully applied in IQA [38,39], image coding [40], video coding [31] and image denoising [41]. Equation (14) suggests that the threshold is chosen adaptively for each patch.…”

Section: Ssim-optimal Local Model From Sparse Representationmentioning

confidence: 99%

SSIM-inspired image restoration using sparse representation

Rehman

Rostami

EURASIP J. Adv. Signal Process.

et al. 2012

Self Cite

Recently, sparse representation based methods have proven to be successful towards solving image restoration problems. The objective of these methods is to use sparsity prior of the underlying signal in terms of some dictionary and achieve optimal performance in terms of mean-squared error, a metric that has been widely criticized in the literature due to its poor performance as a visual quality predictor. In this work, we make one of the first attempts to employ structural similarity (SSIM) index, a more accurate perceptual image measure, by incorporating it into the framework of sparse signal representation and approximation. Specifically, the proposed optimization problem solves for coefficients with minimum L 0 norm and maximum SSIM index value. Furthermore, a gradient descent algorithm is developed to achieve SSIM-optimal compromise in combining the input and sparse dictionary reconstructed images. We demonstrate the performance of the proposed method by using image denoising and super-resolution methods as examples. Our experimental results show that the proposed SSIM-based sparse representation algorithm achieves better SSIM performance and better visual quality than the corresponding least square-based method.

“…Finally we compare the performance of the proposed scheme with two recent RR schemes which we denote as DNT [155] (it is based on divisive normalization transform) and RR SSIM [156]. DNT and RR SSIM respectively require 48 and 36 coefficients from the reference image.…”

Section: Reference Informationmentioning

confidence: 99%

“…We can see that ) (5 Q performs better than RR SSIM for A57, CSIQ and TID databases and achieves competitive performance on LIVE and Toyama databases. It is also fair to mention here that RR SSIM also employs training (which was done using images from LIVE image database) for finding optimal value of the slope parameter (we refer the reader to [156] for details). On the other hand,…”

Section: Reference Informationmentioning

confidence: 99%

See 1 more Smart Citation

Learning based signal quality assessment for multimedia communications

Narwaria¹

Multimedia contents (including image/video, speech, audio, graphic and so on) can be affected by a wide variety of distortions during the process of acquisition, compression, processing, transmission, and reproduction which generally leads to loss of perceptual quality. As a result, signal quality assessment is an important component in today's multimedia communication systems. In this thesis, perceptual quality assessment algorithms are proposed for three important types of multimedia signals, namely image, video, and speech. This involves two crucial stages: (a) feature extraction/detection, and (b) feature pooling.