Perceptual quality assessmentplays a vital role in the visual communication systems owing to theexistence of quality degradations introduced in various stages of visual signalacquisition, compression, transmission and display.Quality assessment for visual signals can be performed subjectively andobjectively, and objective quality assessment is usually preferred owing to itshigh efficiency and easy deployment. A large number of subjective andobjective visual quality assessment studies have been conducted during recent years.In this survey, we give an up-to-date and comprehensivereview of these studies.Specifically, the frequently used subjective image quality assessment databases are firstreviewed, as they serve as the validation set for the objective measures.Second, the objective image quality assessment measures are classified and reviewed according to the applications and the methodologies utilized in the quality measures.Third, the performances of the state-of-the-artquality measures for visual signals are compared with an introduction of theevaluation protocols.This survey provides a general overview of classical algorithms andrecent progresses in the field of perceptual image quality assessment.

Perceptual image quality assessment: a survey

Research Interests Security and privacy: Active authentication, biometrics template protection, biometrics recognition. Computer vision: Domain adaptation, dictionary learning, object recognition, activity recognition, shape representation. Machine learning: Dimensionality reduction, clustering, kernel methods, weakly-supervised learning. Signal/Image processing: Sparse representation, compressive sampling, synthetic aperture radar imaging, millimeter wave imaging.

Sparse and redundant representations for inverse problems and recognition

The compression of hyperspectral images (HSIs) has recently become a very attractive issue for remote sensing applications because of their volumetric data. In this paper, an efficient method for hyperspectral image compression is presented. The proposed algorithm, based on Discrete Wavelet Transform and Tucker Decomposition (DWT-TD), exploits both the spectral and the spatial information in the images. The core idea behind our proposed technique is to apply TD on the DWT coefficients of spectral bands of HSIs. We use DWT to effectively separate HSIs into different sub-images and TD to efficiently compact the energy of sub-images. We evaluate the effect of the proposed method on real HSIs and also compare the results with the well-known compression methods. The obtained results show a better performance of the proposed method. Moreover, we show the impact of compression HSIs on the supervised classification and linear unmixing.

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Compression of Hyperspectral Images Using Discerete Wavelet Transform and Tucker Decomposition

Convolutional neural network (CNN) can be applied in synthetic aperture radar (SAR) object recognition for achieving good performance. However, it requires a large number of the labelled samples in its training phase, and therefore its performance could decrease dramatically when the labelled samples are insufficient. To solve this problem, in this paper, we present a novel active semisupervised CNN algorithm. First, the active learning is used to query the most informative and reliable samples in the unlabelled samples to extend the initial training dataset. Next, a semisupervised method is developed by adding a new regularization term into the loss function of CNN. As a result, the class probability information contained in the unlabelled samples can be maximally utilized. The experimental results on the MSTAR database demonstrate the effectiveness of the proposed algorithm despite the lack of the initial labelled samples.

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A Novel Active Semisupervised Convolutional Neural Network Algorithm for SAR Image Recognition

As one of the most popular dimensionality reduction techniques, locality preserving projections (LPP) has been widely used in computer vision and pattern recognition. However, in practical applications, data is always corrupted by noises. For the corrupted data, samples from the same class may not be distributed in the nearest area, thus LPP may lose its effectiveness. In this paper, it is assumed that data is grossly corrupted and the noise matrix is sparse. Based on these assumptions, we propose a novel dimensionality reduction method, named low-rank preserving projections (LRPP) for image classification. LRPP learns a low-rank weight matrix by projecting the data on a low-dimensional subspace. We use the $\boldsymbol {L_{21}} $ norm as a sparse constraint on the noise matrix and the nuclear norm as a low-rank constraint on the weight matrix. LRPP keeps the global structure of the data during the dimensionality reduction procedure and the learned low rank weight matrix can reduce the disturbance of noises in the data. LRPP can learn a robust subspace from the corrupted data. To verify the performance of LRPP in image dimensionality reduction and classification, we compare LRPP with the state-of-the-art dimensionality reduction methods. The experimental results show the effectiveness and the feasibility of the proposed method with encouraging results.

Low-Rank Preserving Projections

In this letter, we focus on a new compression scheme for synthetic aperture radar (SAR) amplitude images. The last decade has seen a growing interest in the study of dictionary learning and sparse representation, which have been proved to perform well on natural image compression. Because of the special techniques of radar imaging, SAR images have some distinct properties when compared with natural images that can affect the design of a compression method. First, we introduce SAR properties, sparse representation, and dictionary learning theories. Second, we propose a novel SAR image compression scheme by using multiscale dictionaries. The experimental results carried out on amplitude SAR images reveal that, when compared with JPEG, JPEG2000, and a single-scale dictionary-based compression scheme, the proposed method is better for preserving the important features of SAR images with a competitive compression performance.

SAR Image Compression Using Multiscale Dictionary Learning and Sparse Representation

Gradients are commonly used in image sharpness assessment methods.  However, research has not fully addressed the direct relationship between gradients and the perceived sharpness. In this paper, we discover and validate through experiments that the maximum gradient is an effective indicator of the perceived image sharpness on a global or local scale. Based on these observations, we propose a novel and efficient no-reference image quality assessment (NR-IQA) method for blurry images. Our method uses two elements to predict the quality of blurry images: the maximum gradient and the variability of gradients. The maximum gradient represents the sharpest spot in an image, and the variability of gradients shows variations within the content of the image. According to the characteristics of human visual systems, these factors are significant for humans when judging the quality of blurry images. The method was tested using blurry image datasets from five public IQA databases. Compared with nine other state-of-the-art NR-IQA methods for blurry images, the experimental results demonstrate that our method is more consistent with humans’ subjective evaluations. The MATLAB source code of our method is available at https://github.com/Atmegal/Sharpness-evaluation .

No-Reference Image Sharpness Assessment Based on Maximum Gradient and Variability of Gradients

This letter presents a lossless compression algorithm for hyperspectral images, which is based on the strength of correlations between bands. First, a searching model is constructed using the tree structure. Second, multibands which have strong correlations to each chosen band are found out and are then used to predict the chosen band in a couple-group manner. Lastly, residual images are encoded using entropy coders. Experimental results show that our compression algorithm provides a competitive compression performance compared with most existing compression algorithms.

Lossless Compression of Hyperspectral Images Based on Searching Optimal Multibands for Prediction

The development of objective image quality assessment (IQA) metrics aligned with human perception is of fundamental importance to numerous image-processing applications. Recently, human visual system (HVS)-based engineering algorithms have received widespread attention for their low computational complexity and good performance. In this paper, we propose a new IQA model by incorporating these available engineering principles. A local singular value decomposition (SVD) is first utilised as a structural projection tool to select local image distortion features, and then, both perceptual spatial pooling and neural networks (NN) are employed to combine feature vectors to predict a single perceptual quality score. Extensive experiments and cross-validations conducted with three publicly available IQA databases demonstrate the accuracy, consistency, robustness, and stability of the proposed approach compared to state-of-the-art IQA methods, such as Visual Information Fidelity (VIF), Visual Signal to Noise Ratio (VSNR), and Structural Similarity Index (SSIM).

Image quality assessment using a SVD-based structural projection

Subspace learning is one of the main directions for face recognition. In this paper, a novel unsupervised subspace learning method, Neighborhood Preserving Projections (NPP), is proposed. In contrast to traditional linear dimension reduction method, such as principal component analysis (PCA), the proposed method has good neighborhood-preserving property. The central idea is to modify the classical locally linear embedding by introducing a linear transform matrix. The transform matrix is obtained by optimizing a certain objective function. Experimental results on Yale face database and FERET face database show the effectiveness of the proposed method....

Rong Zhang

Papers

SAR Image Compression Using Multiscale Dictionary Learning and Sparse Representation

No-Reference Image Sharpness Assessment Based on Maximum Gradient and Variability of Gradients

Lossless Compression of Hyperspectral Images Based on Searching Optimal Multibands for Prediction

Image quality assessment using a SVD-based structural projection

Face recognition using neighborhood preserving projections