Exploring Level-Wise Interpolation to Improve Lossy Compression Ratio for AMR Applications

2022 ◽  
pp. 259-266
Author(s):  
Yida Li ◽  
Huizhang Luo ◽  
Chubo Liu ◽  
Kenli Li
Keyword(s):  
Compression Ratio ◽  
Lossy Compression

scholarly journals Lossy Images Compression Based on Multiresolution

2018 ◽  
Vol 29 (2) ◽  
pp. 126
Author(s):  
Rana Talib Gdeeb
Keyword(s):  
Polynomial Approximation ◽  
Compression Ratio ◽  
Lossy Compression ◽  
Test Results ◽  
Coding System ◽  
Compression System ◽  
Efficient Coding

A hybrid lossy compression system was presented in this paper. It was based on combining the multiresolution coding together with a polynomial approximation of linear base to decompose grey images followed by an efficient coding. The test results showed promising performance where the compression ratio improved about three times or more compared with the results of the traditional linear predicting coding system.

scholarly journals Enhancing Performance of Lossy Compression on Encrypted Gray Images through Heuristic Optimization of Bitplane Allocation

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2338
Author(s):  
Chuntao Wang ◽  
Renxin Liang ◽  
Shancheng Zhao ◽  
Shan Bian ◽  
Zhimao Lai
Keyword(s):  
Compression Ratio ◽  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Ldpc Code ◽  
Lossy Compression ◽  
Experimental Simulation ◽  
Heuristic Optimization ◽  
Compression Process ◽  
Encrypted Image ◽  
Ldpc Decoding

Nowadays, it remains a major challenge to efficiently compress encrypted images. In this paper, we propose a novel encryption-then-compression (ETC) scheme to enhance the performance of lossy compression on encrypted gray images through heuristic optimization of bitplane allocation. Specifically, in compressing an encrypted image, we take a bitplane as a basic compression unit and formulate the lossy compression task as an optimization problem that maximizes the peak signal-to-noise ratio (PSNR) subject to a given compression ratio. We then develop a heuristic strategy of bitplane allocation to approximately solve this optimization problem, which leverages the asymmetric characteristics of different bitplanes. In particular, an encrypted image is divided into four sub-images. Among them, one sub-image is reserved, while the most significant bitplanes (MSBs) of the other sub-images are selected successively, and so are the second, third, etc., MSBs until a given compression ratio is met. As there exist clear statistical correlations within a bitplane and between adjacent bitplanes, where bitplane denotes those belonging to the first three MSBs, we further use the low-density parity-check (LDPC) code to compress these bitplanes according to the ETC framework. In reconstructing the original image, we first deploy the joint LDPC decoding, decryption, and Markov random field (MRF) exploitation to recover the chosen bitplanes belonging to the first three MSBs in a lossless way, and then apply content-adaptive interpolation to further obtain missing bitplanes and thus discarded pixels, which is symmetric to the encrypted image compression process. Experimental simulation results show that the proposed scheme achieves desirable visual quality of reconstructed images and remarkably outperforms the state-of-the-art ETC methods, which indicates the feasibility and effectiveness of the proposed scheme.

Compression ratio prediction in lossy compression of noisy images

2015 ◽  
Author(s):  
Alexander N. Zemliachenko ◽  
Sergey Abramov ◽  
Vladimir V. Lukin ◽  
Benoit Vozel ◽  
Kacem Chehdi
Keyword(s):  
Compression Ratio ◽  
Lossy Compression ◽  
Noisy Images

scholarly journals Lossy Compression of Multispectral Satellite Images with Application to Crop Thematic Mapping: A HEVC Comparative Study

2020 ◽  
Vol 12 (10) ◽  
pp. 1590 ◽  
Author(s):  
Miloš Radosavljević ◽  
Branko Brkljač ◽  
Predrag Lugonja ◽  
Vladimir Crnojević ◽  
Željen Trpovski ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Compression Ratio ◽  
Satellite Images ◽  
Lossy Compression ◽  
Jpeg 2000 ◽  
Intra Coding ◽  
Sensing Applications ◽  
Multispectral Satellite Images ◽  
Crop Classification ◽  
The Impact

Remote sensing applications have gained in popularity in recent years, which has resulted in vast amounts of data being produced on a daily basis. Managing and delivering large sets of data becomes extremely difficult and resource demanding for the data vendors, but even more for individual users and third party stakeholders. Hence, research in the field of efficient remote sensing data handling and manipulation has become a very active research topic (from both storage and communication perspectives). Driven by the rapid growth in the volume of optical satellite measurements, in this work we explore the lossy compression technique for multispectral satellite images. We give a comprehensive analysis of the High Efficiency Video Coding (HEVC) still-image intra coding part applied to the multispectral image data. Thereafter, we analyze the impact of the distortions introduced by the HEVC’s intra compression in the general case, as well as in the specific context of crop classification application. Results show that HEVC’s intra coding achieves better trade-off between compression gain and image quality, as compared to standard JPEG 2000 solution. On the other hand, this also reflects in the better performance of the designed pixel-based classifier in the analyzed crop classification task. We show that HEVC can obtain up to 150:1 compression ratio, when observing compression in the context of specific application, without significantly losing on classification performance compared to classifier trained and applied on raw data. In comparison, in order to maintain the same performance, JPEG 2000 allows compression ratio up to 70:1.

scholarly journals Adaptive lossy compression of climate model data based on hierarchical tensor with Adaptive-HGFDR (v1.0)

2020 ◽  
Author(s):  
Zhaoyuan Yu ◽  
Zhengfang Zhang ◽  
Dongshuang Li ◽  
Wen Luo ◽  
Yuan Liu ◽  
...  
Keyword(s):  
Data Compression ◽  
Compression Ratio ◽  
Large Scale ◽  
Data Representation ◽  
Lossy Compression ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Model Data ◽  
Compression Parameter

Abstract. Lossy compression has been applied to large-scale experimental model data compression due to its advantages of a high compression ratio. However, few methods consider the uneven distribution of compression errors affecting compression quality. Here we develop an adaptive lossy compression method with the stable compression error for earth system model data based on Hierarchical Geospatial Field Data Representation (HGFDR). We extended the original HGFDR by firstly dividing the original data into a series of the local block according to the exploratory experiment to maximize the local correlations of the data. After that, from the mathematical model of the HGFDR, the relationship between the compression parameter and compression error in HGFDR for each block is analyzed and calculated. Using optimal compression parameter selection rule and an adaptive compression algorithm, our method, the Adaptive-HGFDR, achieved the data compression under the constraints that the compression error is as stable as possible through each dimension. Experiments concerning model data compression are carried out based on the Community Earth System Model (CESM) data. The results show that our method has higher compression ratio and more uniform error distributions, compared with other commonly used lossy compression methods, such as the Fixed-Rate Compressed Floating-Point Arrays method.

Compression Ratio Modeling and Estimation across Error Bounds for Lossy Compression

2020 ◽  
Vol 31 (7) ◽  
pp. 1621-1635
Author(s):  
Jinzhen Wang ◽  
Tong Liu ◽  
Qing Liu ◽  
Xubin He ◽  
Huizhang Luo ◽  
...  
Keyword(s):  
Error Bounds ◽  
Compression Ratio ◽  
Lossy Compression
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