Title
Remote Sensing Image Compression Based on Direction Lifting-Based Block Transform with Content-Driven Quadtree Coding Adaptively.
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.
Year
DOI
Venue
2018
10.3390/rs10070999
REMOTE SENSING
Keywords
Field
DocType
compression,remote sensing images,directional lifting,quadtree coding
Block transform,Computer vision,Remote sensing,Coding (social sciences),Artificial intelligence,Geology,Image compression,Quadtree
Journal
Volume
Issue
ISSN
10
7
2072-4292
Citations 
PageRank 
References 
1
0.37
32
Authors
5
Name
Order
Citations
PageRank
Cuiping Shi182.48
Liguo Wang214328.64
Junping Zhang312433.91
Fengjuan Miao410.71
Peng He510.37