Guangtao Zhai

Using Free Energy Principle For Blind Image Quality Assessment

In this paper we propose a new no-reference (NR) image quality assessment (IQA) metric using the recently revealed free-energy-based brain theory and classical human visual system (HVS)-inspired features. The features used can be divided into three groups. The first involves the features inspired by the free energy principle and the structural degradation model. Furthermore, the free energy theory also reveals that the HVS always tries to infer the meaningful part from the visual stimuli. In terms of this finding, we first predict an image that the HVS perceives from a distorted image based on the free energy theory, then the second group of features is composed of some HVS-inspired features (such as structural information and gradient magnitude) computed using the distorted and predicted images. The third group of features quantifies the possible losses of “naturalness” in the distorted image by fitting the generalized Gaussian distribution to mean subtracted contrast normalized coefficients. After feature extraction, our algorithm utilizes the support vector machine based regression module to derive the overall quality score. Experiments on LIVE, TID2008, CSIQ, IVC, and Toyama databases confirm the effectiveness of our introduced NR IQA metric compared to the state-of-the-art.

Cross-Dimensional Perceptual Quality Assessment for Low Bit-Rate Videos

Most studies in the literature for video quality assessment have been focused on the evaluation of quantized video sequences at fixed and high spatial and temporal resolutions. Only limited work has been reported for assessing video quality under different spatial and temporal resolutions. In this paper, we consider a wider scope of video quality assessment in the sense of considering multiple dimensions. In particular, we address the problem of evaluating perceptual visual quality of low bit-rate videos under different settings and requirements. Extensive subjective view tests for assessing the perceptual quality of low bit-rate videos have been conducted, which cover 150 test scenarios and include five distinctive dimensions: encoder type, video content, bit rate, frame size, and frame rate. Based on the obtained subjective testing results, we perform thorough statistical analysis to study the influence of different dimensions on the perceptual quality and some interesting observations are pointed out. We believe such a study brings new knowledge into the topic of cross-dimensional video quality assessment and it has immediate applications in perceptual video adaptation for scalable video over mobile networks.

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

Proper contrast change can improve the perceptual quality of most images, but it has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, we in this paper first report a new large dedicated contrast-changed image database (CCID2014), which includes 655 images and associated subjective ratings recorded from 22 inexperienced observers. We then present a novel reduced-reference image quality metric for contrast change (RIQMC) using phase congruency and statistics information of the image histogram. Validation of the proposed model is conducted on contrast related CCID2014, TID2008, CSIQ and TID2013 databases, and results justify the superiority and efficiency of RIQMC over a majority of classical and state-of-the-art IQA methods. Furthermore, we combine aforesaid subjective and objective assessments to derive the RIQMC based Optimal HIstogram Mapping (ROHIM) for automatic contrast enhancement, which is shown to outperform recently developed enhancement technologies.

No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics

Contrast distortion is often a determining factor in human perception of image quality, but little investigation has been dedicated to quality assessment of contrast-distorted images without assuming the availability of a perfect-quality reference image. In this letter, we propose a simple but effective method for no-reference quality assessment of contrast distorted images based on the principle of natural scene statistics (NSS). A large scale image database is employed to build NSS models based on moment and entropy features. The quality of a contrast-distorted image is then evaluated based on its unnaturalness characterized by the degree of deviation from the NSS models. Support vector regression (SVR) is employed to predict human mean opinion score (MOS) from multiple NSS features as the input. Experiments based on three publicly available databases demonstrate the promising performance of the proposed method.

No-Reference Image Sharpness Assessment in Autoregressive Parameter Space

In this paper, we propose a new no-reference (NR)/ blind sharpness metric in the autoregressive (AR) parameter space. Our model is established via the analysis of AR model parameters, first calculating the energy- and contrast-differences in the locally estimated AR coefficients in a pointwise way, and then quantifying the image sharpness with percentile pooling to predict the overall score. In addition to the luminance domain, we further consider the inevitable effect of color information on visual perception to sharpness and thereby extend the above model to the widely used YIQ color space. Validation of our technique is conducted on the subsets with blurring artifacts from four large-scale image databases (LIVE, TID2008, CSIQ, and TID2013). Experimental results confirm the superiority and efficiency of our method over existing NR algorithms, the state-of-the-art blind sharpness/blurriness estimators, and classical full-reference quality evaluators. Furthermore, the proposed metric can be also extended to stereoscopic images based on binocular rivalry, and attains remarkably high performance on LIVE3D-I and LIVE3D-II databases.

Automatic Contrast Enhancement Technology With Saliency Preservation

In this paper, we investigate the problem of image contrast enhancement. Most existing relevant technologies often suffer from the drawback of excessive enhancement, thereby introducing noise/artifacts and changing visual attention regions. One frequently used solution is manual parameter tuning, which is, however, impractical for most applications since it is labor intensive and time consuming. In this research, we find that saliency preservation can help produce appropriately enhanced images, i.e., improved contrast without annoying artifacts. We therefore design an automatic contrast enhancement technology with a complete histogram modification framework and an automatic parameter selector. This framework combines the original image, its histogram equalized product, and its visually pleasing version created by a sigmoid transfer function that was developed in our recent work. Then, a visual quality judging criterion is developed based on the concept of saliency preservation, which assists the automatic parameters selection, and finally properly enhanced image can be generated accordingly. We test the proposed scheme on Kodak and Video Quality Experts Group databases, and compare with the classical histogram equalization technique and its variations as well as state-of-the-art contrast enhancement approaches. The experimental results demonstrate that our technique has superior saliency preservation ability and outstanding enhancement effect.

Efficient Image Deblocking Based on Postfiltering in Shifted Windows

We propose a simple yet effective deblocking method for JPEG compressed image through postfiltering in shifted windows (PSW) of image blocks. The MSE is compared between the original image block and the image blocks in shifted windows, so as to decide whether these altered blocks are used in the smoothing procedure. Our research indicates that there exists strong correlation between the optimal mean squared error threshold and the image quality factor Q, which is selected in the encoding end and can be computed from the quantization table embedded in the JPEG file. Also we use the standard deviation of each original block to adjust the threshold locally so as to avoid the over-smoothing of image details. With various image and bit-rate conditions, the processed image exhibits both great visual effect improvement and significant peak signal-to-noise ratio gain with fairly low computational complexity. Extensive experiments and comparison with other deblocking methods are conducted to justify the effectiveness of the proposed PSW method in both objective and subjective measures.

Hybrid No-Reference Quality Metric for Singly and Multiply Distorted Images

In a typical image communication system, the visual signal presented to the end users may undergo the steps of acquisition, compression and transmission which cause the artifacts of blurring, quantization and noise. However, the researches of image quality assessment (IQA) with multiple distortion types are very limited. In this paper, we first introduce a new multiply distorted image database (MDID2013), which is composed of 324 images that are simultaneously corrupted by blurring, JPEG compression and noise injection. We then propose a new six-step blind metric (SISBLIM) for quality assessment of both singly and multiply distorted images. Inspired by the early human visual model and recently revealed free energy based brain theory, our method works to systematically combine the single quality prediction of each emerging distortion type and joint effects of different distortion sources. Comparative studies of the proposed SISBLIM with popular full-reference IQA approaches and start-of-the-art no-reference IQA metrics are conducted on five singly distorted image databases (LIVE, TID2008, CSIQ, IVC, Toyama) and two newly released multiply distorted image databases (LIVEMD, MDID2013). Experimental results confirm the effectiveness of our blind technique. MATLAB codes of the proposed SISBLIM algorithm and MDID2013 database will be available online at http://gvsp.sjtu.edu.cn/.

Three Dimensional Scalable Video Adaptation via User-End Perceptual Quality Assessment

For wireless video streaming, the three dimensional scalabilities (spatial, temporal and SNR) provided by the advanced scalable video coding (SVC) technique can be directly utilized to adapt video streams to dynamic wireless network conditions and heterogeneous wireless devices. However, the question is how to optimally trade off among the three dimensional scalabilities so as to maximize the perceived video quality, given the available resource. In this paper, we propose a low-complexity algorithm that executes at resource-limited user end to quantitatively and perceptually assess video quality under different spatial, temporal and SNR combinations. Based on the video quality measures, we further propose an efficient adaptation algorithm, which dynamically adapts scalable video to a suitable three dimension combination. Experimental results demonstrate the effectiveness of our proposed perceptual video adaptation framework.

Adaptive Sequential Prediction of Multidimensional Signals With Applications to Lossless Image Coding

We investigate the problem of designing adaptive sequential linear predictors for the class of piecewise autoregressive multidimensional signals, and adopt an approach of minimum description length (MDL) to determine the order of the predictor and the support on which the predictor operates. The design objective is to strike a balance between the bias and variance of the prediction errors in the MDL criterion. The predictor design problem is particularly interesting and challenging for multidimensional signals (e.g., images and videos) because of the increased degree of freedom in choosing the predictor support. Our main result is a new technique of sequentializing a multidimensional signal into a sequence of nested contexts of increasing order to facilitate the MDL search for the order and the support shape of the predictor, and the sequentialization is made adaptive on a sample by sample basis. The proposed MDL-based adaptive predictor is applied to lossless image coding, and its performance is empirically established to be the best among all the results that have been published till present.