## 5 Visual Data Processing in WVSNs  
In WVSNs, the visual data includes image and video. The richer content of the visual data makes the visual data processing more challenging than the scalar data processing. Multimedia processing has been an active research field for decades. Feature extraction and processing are the most popular methods used in multimedia processing. The features extracted from the image data include the color, texture and shape. The extracted features from the video data include the motion features that are computed from the pixel variation from consecutive video frames. In multimedia processing, multimedia fusion is also an active research field [39]. It is discussed in [39] that the visual data from multiple camera sensors should be fused and processed in WVSNs. The idea of these traditional multimedia fusion methods is on fusing the different formats of multimedia data to perform certain tasks (e.g., human tracking). These traditional multimedia fusion methods do provide some basic ideas for visual data processing in WVSNs, but traditional multimedia fusion methods are not applicable in limited battery power and limited bandwidth WVSNs. Besides the visual data fusing technique, another interesting technique in visual data processing is image/vision aggregation. In WVSNs, when an event occurs, neighboring camera sensors might capture images/video with high similarities. If the images/video could be combined to eliminate the redundant portions, then system resource (battery and bandwidth) consumption could be reduced. This is known as image/video aggregation. Video aggregation could be realized by multi-view video coding techniques. In [40], the efficiency of multi-view video coding (MVC) and single-view video coding (SVC) in WVSNs are compared. By leveraging the spatial correlation among partially overlapped FoVs, the MVC has advantages in terms of compression efficiency so that the total bandwidth consumption could be reduced. The authors conclude that when there is more than 50% overlapped area and the angular displacement is less than 15 degrees, MVC outperforms SVC. In [41], the idea of image aggregation is realized by using the Square JPEG (S-JPEG) mechanism to reduce the redundant data in the sent images by using the concept of \"Reduced Block Size\" in the two-dimensional discrete cosine transform. The S-JPEG is shown to be more energy efficient than the standard JPEG. However, the image quality could be compromised at the sink, hence, there is a tradeoff between image quality and energy consumption. As compared to JPEG, S-JPEG is more suitable for applications with stringent energy constraints but which do not need a full image quality at the sink (e.g., security surveillance). In [42], the image compression scheme in WVSNs is also studied. An information theoretic image compression framework with the objective of maximizing the compression of the visual data captured in WVSNs is proposed. In this framework, camera sensor nodes are grouped into clusters where each cluster has a cluster head that performs the data encoding. With this kind of clustering, the sensor node could alleviate the problem of computational intensive operations and the cluster head is responsible for handling the correlation between the received images from the sensors in the cluster. The novelty of this image compression framework is that it is independent of the image types and codingalgorithms. From the simulations, they show that the proposed compression framework could save 10% to 23% total coding rate as compared to the scheme where each camera sensor compresses its own image independently. Even though image/video aggregation looks like a promising way to deal with the battery-limited and bandwidth-limited transmission in WVSNs, image/video aggregation requires image/video processing that incurs node processing power. In addition, there is extra MAC layer retransmission energy loss from image/video aggregation.