<title>Correlated image set compression system based on new fast efficient algorithm of Karhunen-Loeve transform</title>

Abstract

The paper presents improved version of our new method for compression of correlated image sets Optimal Image Coding using Karhunen-Loeve transform(OICKL). It is known that Karhunen-Loeve(KL) transform is most optimal representation for such a purpose. The approach is based on fact that every KL basis function gives maximum possible average contribution in every image and this contribution decreases most quickly among all possible bases. So, we lossy compress every KL basis function by Embedded Zerotree Wavelet(EZW) coding with essentially different loss that depends on the ftmctions' contribution in the images. The paper presents new fast low memory consuming algorithm of KL basis construction for compression of correlated image ensembles that enable our OICKL system to work on common hardware. We also present procedure for determining ofoptimal losses ofKL basic functions caused by compression. It uses modified EZW coder which produce whole PSNR(bitrate) curve during the only compression pass.

Advanced Video Coding Systems

“…How to efficiently exploit the correlation among these image or video set to improve the coding efficiency has long been recognized as an open issue (Musatenko and Kurashov 1998). In image set compression, the main issue is how to build the prediction structure, so that the external images and videos in the set can get interpredicted to reduce the redundancy.…”

“…Various methods have been applied to generate the prediction image, such as the Karhunen-Loeve transform (KLT) (Musatenko and Kurashov 1998), centroid-based method and low frequency template-based method (Yeung et al 2011). The advantage of this approach is its low delay and computational complexity, as only the representative image is need to be decoded before actually decoding the target image.…”

“…For a set of similar images, a representative image (e.g., an average image) is selected or generated from them and then compressing the images by subtracting it (Musatenko and Kurashov 1998;. Another class of approaches organizes the similar images as a video sequence according to their correlation, and then compresses the sequence like a video (Chen et al 2004;Zou et al 2013).…”

Image Insertion Using Depth- and Topology- Constrained Minimum Spanning Tree for Compressed Image Sets

“…For any image in the enormous amount of photos, there may be some other images with similar contents, which thus can be called similar images. In order to significantly reduce storage spaces for these numerous similar images, recently clustering methods including Kmeans [6] and affinity propagation (AP) [7], are generally adopted to separate them into groups of similar images called image sets, followed by image set compression algorithms [8]- [35], resulting in compressed image sets. Subsequently, it is necessary for conveniently manipulating compressed image sets to design an image insertion algorithm.…”

Multi-model prediction for image set compression

“…multispectral images [4]. The KLT (KarhuneLoeve Transform)-based method [4], Centroid method [5], max-min differential (MMD) method [6], and max-min predictive (MMP) method [6], have been proposed in previous literature. These schemes usually generate certain types of representative signals (RS) (e.g.…”