Human visual system model

About: Human visual system model is a research topic. Over the lifetime, 8697 publications have been published within this topic receiving 259440 citations.

Single image shadow detection and removal based on feature fusion and multiple dictionary learning

Abstract: In recent years, the analysis of natural image has made great progress while the image of the intrinsic component analysis can solve many computer vision problems, such as the image shadow detection and removal. This paper presents the novel model, which integrates the feature fusion and the multiple dictionary learning. Traditional model can hardly handle the challenge of reserving the removal accuracy while keeping the low time consuming. Inspire by the compressive sensing theory, traditional single dictionary scenario is extended to the multiple condition. The human visual system is more sensitive to the high frequency part of the image, and the high frequency part expresses most of the semantic information of the image. At the same time, the high frequency characteristic of the high and low resolution image is adopted in the dictionary training, which can effectively recover the loss in the high resolution image with high frequency information. This paper presents the integration of compressive sensing model with feature extraction to construct the two-stage methodology. Therefore, the feature fusion algorithm is applied to the dictionary training procedure to finalize the robust model. Simulation results proves the effectiveness of the model, which outperforms compared with the other state-of-the-art algorithms.

From Image Statistics to Scene Gist: Evoked Neural Activity Reveals Transition from Low-Level Natural Image Structure to Scene Category

Abstract: The visual system processes natural scenes in a split second. Part of this process is the extraction of "gist," a global first impression. It is unclear, however, how the human visual system computes this information. Here, we show that, when human observers categorize global information in real-world scenes, the brain exhibits strong sensitivity to low-level summary statistics. Subjects rated a specific instance of a global scene property, naturalness, for a large set of natural scenes while EEG was recorded. For each individual scene, we derived two physiologically plausible summary statistics by spatially pooling local contrast filter outputs: contrast energy (CE), indexing contrast strength, and spatial coherence (SC), indexing scene fragmentation. We show that behavioral performance is directly related to these statistics, with naturalness rating being influenced in particular by SC. At the neural level, both statistics parametrically modulated single-trial event-related potential amplitudes during an early, transient window (100-150 ms), but SC continued to influence activity levels later in time (up to 250 ms). In addition, the magnitude of neural activity that discriminated between man-made versus natural ratings of individual trials was related to SC, but not CE. These results suggest that global scene information may be computed by spatial pooling of responses from early visual areas (e.g., LGN or V1). The increased sensitivity over time to SC in particular, which reflects scene fragmentation, suggests that this statistic is actively exploited to estimate scene naturalness.

Perceptual DFT Watermarking With Improved Detection and Robustness to Geometrical Distortions

Abstract: More than ever, the growing amount of exchanged digital content calls for efficient and practical techniques to protect intellectual property rights. During the past two decades, watermarking techniques have been proposed to embed and detect information within these contents, with four key requirements at hand: robustness, security, capacity, and invisibility. So far, researchers mostly focused on the first three, but seldom addressed the invisibility from a perceptual perspective and instead mostly relied on objective quality metrics. In this paper, a novel DFT watermarking scheme featuring perceptually optimal visibility versus robustness is proposed. The watermark, a noise-like square patch of coefficients, is embedded by substitution within the Fourier domain; the amplitude component adjusts the watermark strength, and the phase component holds the information. A perceptual model of the human visual system (HVS) based on the contrast sensitivity function (CSF) and a local contrast pooling is used to determine the optimal strength at which the mark reaches the visibility threshold. A novel blind detection method is proposed to assess the presence of the watermark. The proposed approach exhibits high robustness to various kinds of attacks, including geometrical distortions. Experimental results show that the robustness of the proposed method is globally slightly better than state-of-the-art. A comparative study was conducted at the visibility threshold (from subjective data) and showed that the obtained performances are more stable across various kinds of content.

End-to-End Blind Image Quality Prediction With Cascaded Deep Neural Network

Abstract: The deep convolutional neural network (CNN) has achieved great success in image recognition. Many image quality assessment (IQA) methods directly use recognition-oriented CNN for quality prediction. However, the properties of IQA task is different from image recognition task. Image recognition should be sensitive to visual content and robust to distortion, while IQA should be sensitive to both distortion and visual content. In this paper, an IQA-oriented CNN method is developed for blind IQA (BIQA), which can efficiently represent the quality degradation. CNN is large-data driven, while the sizes of existing IQA databases are too small for CNN optimization. Thus, a large IQA dataset is firstly established, which includes more than one million distorted images (each image is assigned with a quality score as its substitute of Mean Opinion Score (MOS), abbreviated as pseudo-MOS). Next, inspired by the hierarchical perception mechanism (from local structure to global semantics) in human visual system, a novel IQA-orientated CNN method is designed, in which the hierarchical degradation is considered. Finally, by jointly optimizing the multilevel feature extraction, hierarchical degradation concatenation (HDC) and quality prediction in an end-to-end framework, the Cascaded CNN with HDC (named as CaHDC) is introduced. Experiments on the benchmark IQA databases demonstrate the superiority of CaHDC compared with existing BIQA methods. Meanwhile, the CaHDC (with about 0.73M parameters) is lightweight comparing to other CNN-based BIQA models, which can be easily realized in the microprocessing system. The dataset and source code of the proposed method are available at https://web.xidian.edu.cn/wjj/paper.html .

A modified PSNR metric based on HVS for quality assessment of color images

Abstract: Objective assessment of the image quality is of keen importance in numerous image processing applications Various objective quality assessment indexes have been developed for this purpose, of which Peak signal-to-noise ratio (PSNR) is one of the simplest and commonly used However, it sometimes fails to give result similar to as perceived by the Human Visual System (HVS) This paper presents a modified PSNR metric which is based on HVS characteristics and correlates well with the perceived image quality It takes into account the error sensitivity, structural distortion and edge distortion in the image The proposed metric uses RGB model for color images and empirically combine the effects of above distortion types on each of the color plane Simulation results illustrate the precision and efficiency of the proposed metric in assessing the quality of color images for different types of degradations and show better correlation with the known characteristics of HVS in comparison to conventional PSNR metric