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.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

Reversible Data Hiding for Encrypted Image Based on Interpolation Error Expansion

2018 ◽  
Vol 9 (4) ◽  
pp. 76-96 ◽  
Author(s):  
Fuqiang Di ◽  
Minqing Zhang ◽  
Yingnan Zhang ◽  
Jia Liu
Keyword(s):  
Data Hiding ◽  
Reversible Data Hiding ◽  
Side Information ◽  
Signal To Noise Ratio ◽  
Prediction Method ◽  
Interpolation Error ◽  
Encrypted Image ◽  
Error Expansion ◽  
Pixel Correlation ◽  
Embedding Performance

A novel reversible data hiding algorithm for encrypted image based on interpolation error expansion is proposed. The proposed method is an improved version of Shiu' s. His work does not make full use of the correlation of the neighbor pixels and some additional side information is needed. The proposed method adopts the interpolation prediction method to fully exploit the pixel correlation and employ the Paillier public key encryption method. The algorithm is reversible. In the proposed method, less side information is demanded. The experiment has verified the feasibility and effectiveness of the proposed method, and the better embedding performance can be obtained, compared with some existing RDHEI-P methods. Specifically, the final embedding capacity can be up to 0.74 bpp (bit per pixel), while the peak signal-to-noise ratio (PSNR) for the marked image Lena is 35 dB. This is significantly higher than Shiu's method which is about 0.5 bpp.

scholarly journals Design and Optimization for UAV-Enabled Two-Way Relaying System with SWIPT

2020 ◽  
Author(s):  
Jinlong Wang ◽  
Gang Wang ◽  
Guanyi Chen ◽  
Bo Li ◽  
Ruofei Zhou ◽  
...  
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Power Splitting ◽  
Generalized Singular Value Decomposition ◽  
Ratio Requirement ◽  
Design And Optimization ◽  
Aerial Vehicle ◽  
Resource Allocation Scheme ◽  
Value Decomposition ◽  
Relaying System

Abstract In this paper, we investigate the resource allocation scheme for an unmanned-aerial-vehicle-enable (UAV-enabled) two-way relaying system with simultaneous wireless information and power transfer (SWIPT), where two userequipment exchange information with the help of UAV relay and harvest energythrough power splitting (PS) scheme. Under the transmission power constraintsat UEs and UAV relay, a non-convex intractable optimization problem isformulated which maximizes the sum retained energy of two UEs while satisfying the minimum signal-to-noise ratio requirement. We decouple the complicated beamforming and PS factors optimization problem into three solvable subproblems and propose an efficient alternating optimization scheme. Subsequently, in order to reduce the complexity, a robust scheme based on generalized singular value decomposition (GSVD) is designed. Finally, numerical results verify the robustness and effectiveness of two proposed schemes.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

scholarly journals An SNR-Optimized Scanning Strategy for Geostationary Carbon Cycle Observatory (GeoCarb) Instrument

2018 ◽  
Author(s):  
Jeffrey Nivitanont ◽  
Sean Crowell
Keyword(s):  
Carbon Cycle ◽  
Greenhouse Gases ◽  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Western Hemisphere ◽  
Geostationary Orbit ◽  
Signal To Noise ◽  
Scanning Strategy ◽  
Land Mass ◽  
Error Distributions

Abstract. The Geostationary Carbon Observatory (GeoCarb) will make measurements of greenhouse gases over the land mass in the western hemisphere. The extreme flexibility of observing from geostationary orbit induces an optimization problem, as operators must choose what to observe and when. We express this problem in terms of an optimal subcovering problem, and use an Incremental Optimization (IO) algorithm to create a scanning strategy that minimizes expected error as a function of the signal-to-noise ratio (SNR), and show that this method outperforms the human selected strategy in terms of global error distributions.

A Lossless Distributed Data Compression and Aggregation Approach for Low Resources Wireless Sensors Networks

2021 ◽  
Author(s):  
Elie TAGNE FUTE ◽  
Hugues Marie KAMDJOU ◽  
Adnen EL AMRAOUI ◽  
Armand NZEUKOU
Keyword(s):  
Energy Consumption ◽  
Data Compression ◽  
Signal To Noise Ratio ◽  
Communication Process ◽  
Experimental Simulation ◽  
Small Data ◽  
Distributed Data ◽  
Aggregated Data ◽  
Wireless Medium ◽  
Event Based

Abstract Wireless Sensor Networks (WSN) have been as useful and beneficial as resource-constrained distributed event-based system for several scenarios.Yet, in WSN, optimization oflimited resources (energy, computing memory, bandwidth and storage) during data collection and communication process is a major challenge. Most of energy consumption (as much as 80%) for standard WSN applications lies in the radio module where receiving and sending packets are necessary to communicate between stations.This paper proposes an approach to achieve optimal sensor resources by data compression and aggregation regarding integrity of raw data.Data aggregation discarded a certain sensing data packet, which leads to low data-rate communication and low likelihood of packet collisions on the wireless medium. Data compression reduces a redundancy in aggregated data, which leads to save storage and sending only one small data stream in the bandwidthof communication.The performance of the proposed approach is qualified using experimental simulation on OMNeT++/Castalia. Theperformance metricswere evaluated in terms of Compression Ratio (CR), data Aggregation Rate (AR), Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) and Energy Consumption (EC).The obtained resultshave significantly increased the network lifetime.Moreover, the integrity (quality) of the raw data is guaranteed.

scholarly journals A Blind Signal Samples Detection Algorithm for Accurate Primary User Traffic Estimation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4136
Author(s):  
Jakub Nikonowicz ◽  
Aamir Mahmood ◽  
Mikael Gidlund
Keyword(s):  
Signal To Noise Ratio ◽  
Energy Detection ◽  
Low Complexity ◽  
Primary User ◽  
Time Frame ◽  
Detection Algorithm ◽  
Experimental Simulation ◽  
Spectrum Access ◽  
Blind Signal ◽  
Occupancy Estimation

The energy detection process for enabling opportunistic spectrum access in dynamic primary user (PU) scenarios, where PU changes state from active to inactive at random time instances, requires the estimation of several parameters ranging from noise variance and signal-to-noise ratio (SNR) to instantaneous and average PU activity. A prerequisite to parameter estimation is an accurate extraction of the signal and noise samples in a received signal time frame. In this paper, we propose a low-complexity and accurate signal samples detection algorithm as compared to well-known methods, which is also blind to the PU activity distribution. The proposed algorithm is analyzed in a semi-experimental simulation setup for its accuracy and time complexity in recognizing signal and noise samples, and its use in channel occupancy estimation, under varying occupancy and SNR of the PU signal. The results confirm its suitability for acquiring the necessary information on the dynamic behavior of PU, which is otherwise assumed to be known in the literature.

scholarly journals Uplink Resource Allocation in Device-to-Device Communication System

2018 ◽  
Vol 246 ◽  
pp. 03003
Author(s):  
Xiuwei Han ◽  
Xin Song ◽  
Dong Li ◽  
Jingpu Wang
Keyword(s):  
Resource Allocation ◽  
Power Control ◽  
Channel Assignment ◽  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
System Capacity ◽  
Optimal Selection ◽  
Resource Allocation Problem ◽  
Selection Algorithm ◽  
Signal To Noise

In this paper, we study uplink resource allocation problem to maximize the overall system capacity while guaranteeing the signal-to-noise ratio of both D2D users and cellular users (CUs). The optimization problem can be decomposed into two subproblems: power control and channel assignment. We first prove that the objective function of power control problem is a convex function to get the optimal transmit power. Then, we design an optimal selection algorithm for channel assignment. Numerical results reveal the proposed scheme is capable of improving the system’s performance compared with the random selection algorithm.

scholarly journals Geometric methods for optimal sensor design

2016 ◽  
Vol 472 (2185) ◽  
pp. 20150312 ◽  
Author(s):  
M.-A. Belabbas
Keyword(s):  
Structural Equation ◽  
Optimization Problem ◽  
Dual Problem ◽  
Morse Function ◽  
Ad Hoc ◽  
Signal To Noise Ratio ◽  
Estimation Error ◽  
Gradient Algorithm ◽  
Optimal Estimator ◽  
Actuator Design

The Kalman–Bucy filter is the optimal estimator of the state of a linear dynamical system from sensor measurements. Because its performance is limited by the sensors to which it is paired, it is natural to seek optimal sensors. The resulting optimization problem is however non-convex. Therefore, many ad hoc methods have been used over the years to design sensors in fields ranging from engineering to biology to economics. We show in this paper how to obtain optimal sensors for the Kalman filter. Precisely, we provide a structural equation that characterizes optimal sensors. We furthermore provide a gradient algorithm and prove its convergence to the optimal sensor. This optimal sensor yields the lowest possible estimation error for measurements with a fixed signal-to-noise ratio. The results of the paper are proved by reducing the optimal sensor problem to an optimization problem on a Grassmannian manifold and proving that the function to be minimized is a Morse function with a unique minimum. The results presented here also apply to the dual problem of optimal actuator design.

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