Wavelets provide an attractive method for efficient image compression. For transmission across noisy or bandwidth
limited channels, a signal may be subjected to quantization in which the signal is transcribed onto a
reduced alphabet in order to save bandwidth. Unfortunately, the performance of the discrete wavelet transform
(DWT) degrades at increasing levels of quantization. In recent years, evolutionary algorithms (EAs) have been
employed to optimize wavelet-inspired transform filters to improve compression performance in the presence of
quantization. Wavelet filters consist of a pair of real-valued coefficient sets; one set represents the compression
filter while the other set defines the image reconstruction filter. The reconstruction filter is defined as the
biorthogonal inverse of the compression filter. Previous research focused upon two approaches to filter optimization.
In one approach, the original wavelet filter is used for image compression while the reconstruction
filter is evolved by an EA. In the second approach, both the compression and reconstruction filters are evolved.
In both cases, the filters are not biorthogonally related to one another. We propose a novel approach to filter
evolution. The EA optimizes a compression filter. Rather than using a wavelet filter or evolving a second filter
for reconstruction, the reconstruction filter is computed as the biorthogonal inverse of the evolved compression
filter. The resulting filter pair retains some of the mathematical properties of wavelets. This paper compares
this new approach to existing filter optimization approaches to determine its suitability for the optimization of
image filters appropriate for defense applications of image processing.
The development of novel image processing algorithms requires a diverse and relevant set of training images
to ensure the general applicability of such algorithms for their required tasks. Images must be appropriately
chosen for the algorithm's intended applications. Image processing algorithms often employ the discrete wavelet
transform (DWT) algorithm to provide efficient compression and near-perfect reconstruction of image data.
Defense applications often require the transmission of images and video across noisy or low-bandwidth channels.
Unfortunately, the DWT algorithm's performance deteriorates in the presence of noise. Evolutionary algorithms
are often able to train image filters that outperform DWT filters in noisy environments. Here, we present and
evaluate two image sets suitable for the training of such filters for satellite and unmanned aerial vehicle imagery
applications. We demonstrate the use of the first image set as a training platform for evolutionary algorithms that
optimize discrete wavelet transform (DWT)-based image transform filters for satellite image compression. We
evaluate the suitability of each image as a training image during optimization. Each image is ranked according
to its suitability as a training image and its difficulty as a test image. The second image set provides a test-bed
for holdout validation of trained image filters. These images are used to independently verify that trained filters
will provide strong performance on unseen satellite images. Collectively, these image sets are suitable for the
development of image processing algorithms for satellite and reconnaissance imagery applications.