Perceptual image quality assessment metric using mutual information of Gabor features

Ding, Yong; Zhang, Yuan; Wang, Xiang; Yan, Xiaolang; Krylov, Andrey S.

doi:10.1007/s11432-013-4881-y

Cited by 11 publications

(6 citation statements)

References 23 publications

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“…In the absence of a perfect psychophysically defined error measure [34], for each quantized image, we report quantitive results under various perceptual metrics including the most popular mean squared error (MSE), structural similarity (SSIM) [35], Puzicha's spatial quantization error (SQE) [6] and the edge-aware spatial quantization error (ESQE) derived from our cost function. In the comparison, we set the dithering level in the Floyd Steinberg method to 1.0, the number of iterations in k-means to 10, and the number of iterations in MMC to 30.…”

Section: Methodsmentioning

confidence: 99%

Efficient, Edge-Aware, Combined Color Quantization and Dithering

Huang

Martin

et al. 2016

IEEE Trans. on Image Process.

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Abstract-In this paper we present a novel algorithm to simultaneously accomplish color quantization and dithering of images. This is achieved by minimizing a perception-based cost function which considers pixel-wise differences between filtered versions of the quantized image and the input image. We use edge aware filters in defining the cost function to avoid mixing colors on opposite sides of an edge. The importance of each pixel is weighted according to its saliency. To rapidly minimize the cost function, we use a modified multi-scale iterative conditional mode (ICM) algorithm which updates one pixel a time while keeping other pixels unchanged. As ICM is a local method, careful initialization is required to prevent termination at a local minimum far from the global one. To address this problem, we initialize ICM with a palette generated by a modified mediancut method. Compared to previous approaches, our method can produce high quality results with fewer visual artifacts but also requires significantly less computational effort.Index Terms-Color quantization, dithering, optimizationbased image processing.

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Section: Methodsmentioning

confidence: 99%

Efficient, Edge-Aware, Combined Color Quantization and Dithering

Huang

Martin

et al. 2016

IEEE Trans. on Image Process.

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“…Furthermore, it has been found that Gabor filters have excellent properties and the shapes of Gabor wavelets are similar to the receptive fields of simple cells in the primary visual cortex. Thus, visual features are extracted by 2D Gabor filter to reflect the nonlinear mechanism of HVS [18]. Instead of simulating the functional components of the low-level HVS, some high-level aspects of HVS, such as visual attention and visual saliency, are considered into feature extraction [9,19].…”

Section: Related Workmentioning

confidence: 99%

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Shang

Zhao

Ding

2018

Journal of Engineering

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Image quality assessment that aims to evaluate the image quality automatically by a computational model plays a significant role in image processing systems. To meet the need of accuracy and effectiveness, in the proposed method, complementary features including histogram of oriented gradient, edge information, and color information are employed for joint representation of the image quality. Afterwards, the dissimilarities of the extracted features between the distorted and reference images are quantified. Finally, support vector regression is used for distortion indices fusion and objective quality mapping. Experimental results validate that the proposed method outperforms the state-of-the-art methods in terms of consistency with subjective perception and robustness across various databases and different distortion types.

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“…Local features extracting methods usually filter an image with a given operator, such as Gabor [7][8][9], Fourier transforms [10], Hough transforms [11], self-feedback template [12], LBP (Local Binary Pattern) [13][14], Wavelet [15][16] and other filters [17][18], etc. In [7], the authors used Log-Gabor filter to detect the edge-oriented urban characteristics, and two Log-Gabor filter response images to suppress the noise and acquire a smooth spatial region.…”

Section: Research Statusesmentioning

confidence: 99%

Building Objects Extracting and Recognizing Methodology from Satellite Image based on the Feature Extraction Algorithms and Sliding Window Technique

Yang¹,

Shen²

2016

IJSIP

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Perceptual image quality assessment metric using mutual information of Gabor features

Abstract: Information-based reduced reference image quality assessment incorporating non-tensor product wavelet filter banks

Cited by 11 publications

References 23 publications

Efficient, Edge-Aware, Combined Color Quantization and Dithering

Efficient, Edge-Aware, Combined Color Quantization and Dithering

Image Quality Assessment Based on Joint Quality-Aware Representation Construction in Multiple Domains

Building Objects Extracting and Recognizing Methodology from Satellite Image based on the Feature Extraction Algorithms and Sliding Window Technique

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