Abstract
Due to the limitations of storage and transmission in remote sensing scenarios, lossy compression techniques have been commonly considered for remote sensing images. Inspired by the latest development in image coding techniques, we present in this paper a new compression framework, which combines the directional adaptive lifting partitioned block transform (DAL-PBT) with content-driven quadtree codec with optimized truncation (CQOT). First, the DAL-PBT model is designed; it calculates the optimal prediction directions of each image block and performs the weighted directional adaptive interpolation during the process of directional lifting. Secondly, the CQOT method is proposed, which provides different scanning orders among and within blocks based on image content, and encodes those blocks with a quadtree codec with optimized truncation. The two phases are closely related: the former is devoted to image representation for preserving more directional information of remote sensing images, and the latter leverages adaptive scanning on the transformed image blocks to further improve coding efficiency. The proposed method supports various progressive transmission modes. Experimental results show that the proposed method outperforms not only the mainstream compression methods, such as JPEG2000 and CCSDS, but also, in terms of some evaluation indexes, some state-of-the-art compression methods presented recently.
Highlights
Along with the rapid development of remote sensing technology, it is becoming easier to acquire high spatial resolution remote sensing images from various satellites and sensors, which is undoubtedly beneficial to the application of remote sensing images [1,2]
Traditional compression methods can be used for the compression of remote sensing images, such as Embedded Zerotree Wavelet (EZW) [3], Set Partitioning in Hierarchical Tree (SPIHT) [4], Set Partitioned Embedded Block Coder (SPECK) [5], and Joint Photographic Experts Group 2000 (JPEG2000) [6], or some improved versions of them [7,8,9,10]
Embedded block coding with optimized truncation (EBCOT) is an effective coding method, which is the fundamental part of the JPEG2000 image compression standard
Summary
Along with the rapid development of remote sensing technology, it is becoming easier to acquire high spatial resolution remote sensing images from various satellites and sensors, which is undoubtedly beneficial to the application of remote sensing images [1,2]. In [15] the authors extend the JPEG2000 for the compression of remote sensing images These compression methods are based on Discrete Wavelet Transform (DWT). In [33], the authors propose a sparse representation-based compression method for hyperspectral images, which tends to represent hyperspectral data with a learned dictionary via sparse coding. Embedded block coding with optimized truncation (EBCOT) is an effective coding method, which is the fundamental part of the JPEG2000 image compression standard. It divides each transformed subband into many independent code-blocks, encodes these code-blocks, and performs optimal truncation based on the specified bit rate, respectively. TinhteerDpAolLat-ePdBTremmoodteelseisndsiensgcriimbeadgeins.dTehteaiDl aAsLfo-PllBoTwms. odel is described in detail as follows
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