Selective sampling for compression and effective reconstruction
The current standard when creating a digital image is to have a grid of sensors with each sensor collecting information that defines its associated pixel value in a digital image. For many applications storing or transmitting the entire set of pixel values is not desirable due to its large size. In these cases processing is performed on the original data which reduces its size, but results in an image which is an approximation of the original. We present an alternative to this approach that will only use a subset of the existing sensors, thus collecting less initial data and therefore avoiding the need for the additional step of having to reduce the data size after sampling. For this approach, which uses sub-sampling, we define an image construction method which takes the sub-samples and produces its own approximation of the fully sampled image. With this construction method and a strict constraint on the kinds of images we are allowed to capture, we show that there exists an optimal set of pixel locations to sample to minimize the squared error between the constructed image and the fully sampled image. Since our method has less information about the image when it decides how to reduce its size, when compared to the current standard, our method will result in a less accurate approximation of the original image. Even with this lowered performance we will show that using our supplied construction method and sample locations under the specified constraint on the original image, the proposed method creates an image that is a visually acceptable approximation.^
Colin F Merrick,
"Selective sampling for compression and effective reconstruction"
Dissertations and Master's Theses (Campus Access).