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)