Remote sensing image compression based on binary tree and optimized truncation

Huang, Ke-Kun; Ren, Chuan; Yu, Yu Feng; Lai, Zhao Rong

doi:10.1016/j.dsp.2017.02.008

Cited by 11 publications

(9 citation statements)

References 34 publications

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“…The goodness of the optimal compression method is validated with respect to CR, compression factor (CF), bit rate, MSE, root mean square error (RMSE), peak signal to noise ratio (PSNR), and SS. For comparison purposes, a set of three methods are used, namely, BTOT, 23 JPEG, and JPEG2000.…”

Section: Resultsmentioning

confidence: 99%

Optimal deep learning based image compression technique for data transmission on industrial Internet of things applications

Sujitha

Velmurugan

Lydia

et al. 2020

Trans Emerging Tel Tech

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Recently, industrial Internet of things becomes more popular and it involves a group of intelligent devices linked to create systems which observe, gather, communicate, and investigate data. In this view, the demand for compression techniques in remote sensing images is increasing since low complexity technique is required in spacecraft. Deep learning, for instance, convolutional neural network (CNN) has gained more attention in the domain of computer vision, particularly for high‐level applications like detection along with interpretation. At the same time, it is difficult to resolve the low‐level applications like image compression and it is investigated in this article. This article presents an optimal compression technique using CNNs for remote sensing images. The proposed method uses CNN for learning the compact representation of the original image which held the structural data and was then coded by Lempel Ziv Markov chain algorithm. Next, the encoded image was reconstructed to retrieve the original image with high reconstructed image quality. The proposed optimal compression technique is compatible with the available image codec standards. Wide range of experiments was carried out and the results were compared with binary tree and optimized truncation, JPEG, and JPEG2000 in terms of compression efficiency, reconstructed image quality, and space saving (SS). The obtained results apparently proved the effectiveness of the presented method, which attains an average peak signal to noise ratio of 49.90 dB and SS of 89.38%.

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Section: Resultsmentioning

confidence: 99%

Optimal deep learning based image compression technique for data transmission on industrial Internet of things applications

Sujitha

Velmurugan

Lydia

et al. 2020

Trans Emerging Tel Tech

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show abstract

“…The preprocessing step is includes removing the black area surrounding the obtained satellite image and enhance satellite image contrast. This step is very necessary to increase the accuracy of registration operation; figures (5,6) show the effect of using this process on Image band 1. To evaluate the transformation operation, the measures (Mean Square Error MSE, Mean Absolute Error MAE) are computed according to the following formula [4]:…”

Section: Resultsmentioning

confidence: 99%

“…The proposed technique was compared with traditional satellite image compression techniques and the results show lower impact than that of traditional techniques.  Ke -Kun H. et al [6] proposed an improved binary tree coding with adaptive scanning order (BTCA) by optimized truncation. He used 9/7-tap biorthogonal wavelet filters for wavelet transform then divided the image into several Licensed Under Creative Commons Attribution CC BY blocks to be encoded individually, then he optimized truncation to obtain higher compression ratio and less memory space  Meishan Li et al [7] performed three-level wavelet on remote sensing images using biorthogonal wavelet ,then used Set Partitioning In Hierarchical Trees (SPIHT) Algorithm which improved by fixing the threshold to compress image at fixed scale, then compare the obtained results with Embedded Zero-tree Wavelets (EZW) algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In this paper the applied transformation was the 1st order affine transformation which represent a combination of linear and translation transformation. This transformation has six unknown parameters two for translation, one for rotation, one for scaling, one for scaling direction, and one for scaling ratio, and can be define as [3]: x = m1+m2X+m3Y (5) y = n1+n2X+n3Y (6) Where: (m1, m2, m3) and (n1, n2, n3) are the transformation coefficients.…”

Section: Image Registrationmentioning

confidence: 99%

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Lossy Compression for Raw Satellite Images Using Intra Frame and Inter Frame Coding

2017

IJSR

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Abstract:The huge size of the satellite images data leads to big problems in storage capacity and power exhaustion for the transmission, therefore great efforts have been made to find low-complexity compression algorithms. To overcome these issues in this field we have proposed satellite image compression method based on inter-intra frame coding, to compress sequence of satellite images captured at different times, form different sensors, or /and different view points for the same area. Inter-frame coding principle based on removing temporal redundancy exists between two successive satellite images: a historical captured image (reference image) and a new captured image (sensed image) for the same scene. Since sequential satellite images contain almost same materials with only minor movements according to the time interval between them (several days, several months). The resulting difference's image after subtraction has much lower entropy and can be compressed with much less cost than the original sensed satellite image. This is followed by removing still existing redundancy of pixels within resulting difference's image using principle intra-frame coding, and this will be helpful in order to satisfy high compression performance while keeping the quality of the compressed satellite image as much as possible.

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“…This means that some traditional methods that rarely consider high-frequency information and mainly focus on retaining more low-frequency information rate, respectively. However, the EBCOT method only considers the correlation within a subband, and there are a lot of truncated points that need to be stored during the process of running the algorithm [9,36].…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Compression Based on Direction Lifting-Based Block Transform with Content-Driven Quadtree Coding Adaptively

et al. 2018

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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.

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Remote sensing image compression based on binary tree and optimized truncation

Cited by 11 publications

References 34 publications

Optimal deep learning based image compression technique for data transmission on industrial Internet of things applications

Optimal deep learning based image compression technique for data transmission on industrial Internet of things applications

Lossy Compression for Raw Satellite Images Using Intra Frame and Inter Frame Coding

Remote Sensing Image Compression Based on Direction Lifting-Based Block Transform with Content-Driven Quadtree Coding Adaptively

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