GMVP: gradient magnitude and variance pooling-based image quality assessment in sensor networks

EURASIP Journal on Wireless Communications and Networking, Jan 2016

In this paper, we focus on image quality assessment (IQA) in sensor networks and propose a novel method named gradient magnitude and variance pooling (GMVP). The proposed GMVP follows a two-step framework. In this first step, we utilize gradient magnitude to compute the local quality, which is efficient and responsive to degeneration when the images are transmitted by sensor networks. In the second step, we propose a weighted pooling operation, i.e., variance pooling, which explicitly considers the importance of different local regions. The variance pooling operation assigns different weights to local quality map according to the variance of local regions. The proposed GMVP is verified on two challenging IQA databases (CSIQ and TID 2008 databases), and the results demonstrate that the proposed GMVP achieves better results than the state-of-the-art methods in sensor networks.

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GMVP: gradient magnitude and variance pooling-based image quality assessment in sensor networks

Zhang and Liu EURASIP Journal on Wireless Communications and Networking GMVP: gradient magnitude and variance pooling-based image quality assessment in sensor networks Zhong Zhang 0 Shuang Liu 0 0 College of Electronic and Communication Engineering, Tianjin Normal University , Tianjin , China In this paper, we focus on image quality assessment (IQA) in sensor networks and propose a novel method named gradient magnitude and variance pooling (GMVP). The proposed GMVP follows a two-step framework. In this first step, we utilize gradient magnitude to compute the local quality, which is efficient and responsive to degeneration when the images are transmitted by sensor networks. In the second step, we propose a weighted pooling operation , i.e., variance pooling, which explicitly considers the importance of different local regions. The variance pooling operation assigns different weights to local quality map according to the variance of local regions. The proposed GMVP is verified on two challenging IQA databases (CSIQ and TID 2008 databases), and the results demonstrate that the proposed GMVP achieves better results than the state-of-the-art methods in sensor networks. Sensor networks; Image quality assessment; Variance pooling; Gradient magnitude 1 Introduction With the rapid development of wireless communications and electronics, sensor networks have received much attention in research fields [ 1, 2 ]. The wireless sensor network (WSN) consists of a variety of sensors, such as video cameras, microphones, infrared badges and RFID tags, which drives the applications of WSN in the fields of surveillance systems, guiding systems, biological detection, habitat, agriculture, and health monitoring. There are a mount of images transmitted in sensor networks. Thus, finding ways to test the performance of sensor networks about the transmitted image quality has provoked great interests in research fields. In this paper, we focus on image quality assessment (IQA) for testing sensor network. Human beings are the final observers of the transmitted images, and therefore, they are entitled to evaluate the image quality as shown in Fig. 1. Hence, the target of IQA is to develop automatic methods that can predict image quality consistently with human subjective evaluation. There are three kinds of IQA models in terms of the availability of a reference image: full reference (FR) models where the pristine reference image is available, reduced reference (RR) models where only a small fraction of reference information is available, and no reference (NF) models where the reference image is unavailable. This paper only discusses FR-IQA models which can be widely used to evaluate the performance of image transmission system, e.g., sensor networks, by measuring the quality of their output images. Generally speaking, FR-IQA models can be classified into two types. The first one is built under a bottom-up framework [ 3–5 ] which simulates the various processing stages in the visual pathway of human visual system (HVS), including just noticeable differences [ 6 ], visual masking effect [ 7 ], etc. Nevertheless, HVS is too intricate to construct an accurate bottom-up FR-IQA framework. The second one constructs a top-down framework [ 8–11 ] which designs to model the overall function of HVS according to some global assumption. Recent studies [ 8, 9 ] have demonstrated the effectiveness of these kinds of methods, and thus, many approaches follow the top-down framework. The structural similarity (SSIM) [ 12 ], as a representative approach of top-down model, is based on the assumption that HVS is highly adapted to extract the structural information from the visual scene, and thus, a measurement of SSIM should provide a good approximation of image quality. The improvements of SSIM, for example, multi-scale structural similarity (MSSSIM) [ 13 ], three-component weighted SSIM (3-SSIM) [ 14 ], and information-weighted SSIM [ 15 ] also employ the same assumption and achieve better results than original SSIM. Moreover, information fidelity criteria (IFC) [ 16 ] and visual information fidelity (VIF) [ 17 ] regard HVS as a communication channel. The subjective image quality is predicted by computing how much the information in the reference image is preserved in the transmitted one. From another point of view, many FR-IQA models consist of two stages [ 15, 18, 19 ] as shown in Fig. 2. The first step is local similarity computation which is calculated by locally comparing the transmitted image with the reference image according to some similarity function. Considering the computational complexity, many approaches adopt image gradient as a measurement feature [ 20–22 ] due to effectively capturing image local structure which is incentive to HSV. Most gradient-based FR-IQA models [ 8, 9 ] are inspired by SSIM [12]. They first compute the similarity between the gradients of the reference image and transmitted image an (...truncated)


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Zhong Zhang, Shuang Liu. GMVP: gradient magnitude and variance pooling-based image quality assessment in sensor networks, EURASIP Journal on Wireless Communications and Networking, 2016, pp. 15, Volume 2016, Issue 1, DOI: 10.1186/s13638-015-0477-0