Image encryption utilizing lossy image compression

This paper presents a survey on image encryption and compression techniques. The main aim of using encryption and compression together is to reduce the size; to ensure a quality during reconstruction; to reduce bandwidth requirement, to increase the speed of data communication over a network; to reduce the computational time, and to ensure its safety. To carry out the Image compression and encryption together three different ways are there. The three existing combinations are as encryption technique followed by compression, in this method compression ratio is more, but there are some security issues as compared with next. The next combination is a compression technique followed by encryption technique, in this method, researchers focus more on security of the image rather than to compress the size of the image. The last combination is joint encryption and compression strategies in this strategy encryption and compression happens at the same time either to some extent or as compressive detecting. With low computational time it gives a decent security. The compression procedure utilized in every one of the blends can be lossless, lossy, or mix of both.

Pre-processing of medical images plays a vibrant part in the field of medicine to detect patient’s disease at an earlier stage. Hospitals and medical centers generate an enormous volume of digital medical images day by day, which is used for several purposes of diagnostic procedures. Because of many images,for secured transmission, image compression is required to reduce the redundancies in the image and to accomplish the proficient image communication. To reduce the redundancies in the image and to accomplish the proficient communication of images. A competent Adaptive sigma filterized synorr certificateless signcryptive Levenshtein entropy coding-based deep neural learning (ASFSCSLEC-DNL) technique is presented to develop the image encryption and compression. The main goal of the ASFSCSLEC-DNL technique was to improve the security level of medical image transmission. The deep feed-forward artificial neural network was applied in the ASFSCSLEC-DNL technique for medical image pre-processing, encryption, and compression with multiple layers. The adaptive sigma filter was employed to denoise the medical image. The medical image encryption and signature generation were done with synorr certificateless signcryption. Finally, Levenshtein entropy encoding was applied to compress images. Then the compressed image was sent to the receiver where the decompression and decryption are implemented using Levenshtein entropy decoding and synorr certificateless decryption. Investigational estimation was carried out in chest X-ray medical images and the results of ASFSCSLEC-DNL technique proved more capable in terms of higher peak signal to noise ratio and compression ratio with lesser encryption time compared to the existing state-of-the-art methods.

In the last decade, the communication of images through the internet has increased. Due to the growing demands for data transfer through images, protection of data and safe communication is very important. For this purpose, many encryption techniques have been designed and developed. New and secured encryption schemes based on chaos theory have introduced methods for secure as well as fast communication. A modified image encryption process is proposed in this work with chaotic maps and orthogonal matrix in Hill cipher. Image encryption involves three phases. In the first phase, a chaotic Henon map is used for permuting the digital image. In the second phase, a Hill cipher is used whose encryption key is generated by an orthogonal matrix which further is produced from the equation of the plane. In the third phase, a sequence is generated by a chaotic tent map which is later XORed. Chaotic maps play an important role in the encryption process. To deal with the issues of fast and highly secured image processing, the prominent properties of non-periodical movement and non-convergence of chaotic theory play an important role. The proposed scheme is resistant to different attacks on the cipher image. Different tests have been applied to evaluate the proposed technique. The results of the tests such as key space analysis, key sensitivity analysis, and information entropy, histogram correlation of the adjacent pixels, number of pixel change rate (NPCR), peak signal to noise ratio (PSNR), and unified average changing intensity (UCAI) showed that our proposed scheme is an efficient encryption technique. The proposed approach is also compared with some state-of-the-art image encryption techniques. In the view of statistical analysis, we claim that our proposed encryption algorithm is secured.

In the field of agriculture, image processing plays a significant role in which Image compression and Encryption is the most important tool. Image processing provides applications for agriculture in detection of unwanted growth of crops and its health monitoring. Image Encryption and Compression techniques are utilized to achieve better reconstructed image. Generally, remote sensing equipment is utilized in broadcast of images that are captured in the agriculture field. During transmission, image size and bandwidth are the main issues which require huge storage space and large bandwidth for the transmission. Hence, it is obligatory to compress the image before transmission. Various compression and Encryption methodologies are used so far. But there are some limitations of the existing techniques like high encryption time and quality of reconstructed image will be affected. Therefore, to overwhelm these limitations Novel Homomorphic Encryption algorithm (NHE) for encryption process and an Enhanced Discrete Wavelet Transform (EDWT) for compression procedure (NHE-EDWT) is proposed. The Image is encrypted, compressed, decompressed, and finally decrypted to get the resultant image. The advantage of using this enhanced methodology diminishes the time taken for encryption and retains a better quality reconstructed image. Finally, the performances are measured for peak signal to noise ratio (PSNR), Execution Time, and Compression ratio. On comparing with the existing methodologies, this proposed work offers better efficiency in PSNR approximately 15.06% with classic methods, Compression ratio is improved about 84.02%, and Encryption time is decreased about 89.70%. The quality of reconstructed images improved, the size of the image is reduced by improving the compression ratio. Thus reduces the required bandwidth for image transmission. Decreased encryption time reduces the time consumption for computation.

Images and video-based multimedia data are growing rapidly due to communication network technology. During image compression and transmission, images are inevitably corrupted by noise due to the influence of the environment, transmission channels, and other factors, resulting in the damage and degradation of digital images. Numerous real-time applications, such as digital photography, traffic monitoring, obstacle detection, surveillance applications, automated character recognition, etc are affected by this information loss. Therefore, the efficient and safe transmission of data has become a vital study area. In this research, an image compression–encryption system is proposed to achieve security with low bandwidth and image de-noising issues during image transmission. The Chevrolet transformation is proposed to improve image compression quality, reduce storage space, and enhance de- noising. A 3D chaotic logistic map with DNA encoding and Tuna Swarm Optimization is employed for innovative image encryption. This optimization approach may significantly increase the image's encryption speed and transmission security. The proposed system is built using the Xilinx system generator tool on a field-programmable gate array (FPGA). Experimental analysis and experimental findings show the reliability and scalability of the image compression and encryption technique designed. For different images, the security analysis is performed using several metrics and attains 32.33 dB PSNR, 0.98 SSIM, and 7.99721 information entropy. According to the simulation results, the implemented work is more secure and reduces image redundancy more than existing methods.

The compression and encryption of images is essential to securely transmit high quality images over the optical network. In earlier studies, an optical image encryption using Loxodromic cat map with improved double random phase encoding (LCMIDRPE) has been developed. Hilbert huang transform (HHT) used in this encoding model decrease deviations of encrypted images. However, efficient image compression technique is required for compression. An efficient optical image compression and encryption (OICE) technique is developed in this paper by Direction-adaptive discrete wavelet transform (DA-DWT) along with directional lifting and LCMIDRPE. The original image is compressed using the DA-DWT with directional lifting and then encrypted by LCMIDRPE. The inverse version of LCMIDRPE and DA-DWT regain the original image. The missed information's due to lossy compression, decompression, encryption, decryption and transmission of images degrade the quality of regained images. The regained image is enhanced using intensified convolutional neural network (ICNNet). The whole process is termed to be LCMIDRPE-DADWT-ICNNet. Finally, the experimental results exhibits that the LCMIDRPE-DADWT-ICNNet model achieves correlation coefficient (CC) of 0.98 , Peak signal-to-noise ratio (PSNR) of 97db, Mean absolute difference (MAD) of 20 and mean square error (MSE) of 0.45 which is higher than OICE methods like Compressive Sensing and Rivest-Shamir-Adleman method (CS-RSA), Fresnel Diffraction and Discrete Wavelet Transform (FD-DWT), 2Dimentional Sparse Representation and Chaotic Map (2DSR-CM), 2Dimensional Compressive Sensing and Hyperchaotic System (2DCS-HS), Diffractive-Imaging-Based Encryption (DIBE) and Content-Adaptive Image Compression and Encryption using Optimized Compressive Sensing with Double Random Phase Encoding (CAIE-OCS-DPRE) methods.

Image compression is a very important useful technique for efficient transmission as well as storage of images. The demand for communication of multimedia data through the telecommunication network and accessing the multimedia data through internet by utilizing less bandwidth for communication is growing explosively. Basically the image data comprise of significant portion of multimedia data and they occupy maximum portion of communication bandwidth for multimedia communication. Therefore the development of efficient image compression technique is quite necessary. The 2D Haar wavelet transform along with Hard Thresholding and Run Length Encoding is one of the efficient proposed image compression technique. JPEG2000 is a standard image compression method capable of producing very high quality compressed images. Conventional Run Length Encoding(CRLE),Optimized Run Length Encoding(ORLE),Enhanced Run Length Encoding(ERLE) are different types of RLES applied on both proposed method of compression and JPEG2000. Conventional Run Length Encoding produces efficient result for proposed method whereas Enhanced Run Length Encoding produces efficient result in JPEG2000 compression. This is the novel approach that the authors have proposed for compression of image using compression ratio (CR) without losing the PSNR, quality of image using lesser bandwidth.

A method called A Robust Chaos-Based Technique for Medical Image Encryption was created to improve the security of medical image storage and transmission. Because they include private patient information, medical photos are susceptible to illegal access. In order to safeguard pictures like MRIs, CT scans, and X-rays, this project uses chaos-based encryption algorithms. By generating encryption keys using the Henon chaotic map, the system ensures great security through unpredictability and randomness. To improve anonymity, image preprocessing methods like pixel substitution and permutation are used. Histogram analysis, correlation analysis, and entropy computations are used to assess the security performance of the encrypted images. An effective way to protect medical data in contemporary healthcare systems, this approach combines the ideas of cryptography and chaos theory to provide strong protection against unwanted access. Keywords: Histogram analysis, security, diagnostics, encrypted images, medical images, Henon Chaotic Map, and cryptosystem.

In recent years, there have been increasing demands for applications of image storage, sharing and transmission across data networks. For these data-intensive and sensitive applications, it is imperative to perform both image compression and encryption. In this paper, two different approaches of image compression and encryption were studied and compared. In the first approach, image compression was followed by encryption; while, in the second approach, image encryption was followed by compression. Both approaches were implemented based on standard schemes, i.e., JPEG 2000 lossless compression and DES encryption. Experiments were conducted for two different types of testing images: a natural image and a medical image. The efficiency performance of the two approaches was compared based on key parameters such as MSE, PSNR, entropy, image file size, compression ratio and time consumption. We found that the first approach outperformed the second for both testing images. The results are presented and analysed along with recommendations for related future work.

Although JPEG technique is considered as the most popular image compression standard, it behaves high visual degradation at low bit rates. In this paper, an efficient DCT–based image compression technique is proposed to achieve high Compression Ratio (CR) with high quality at both high and low bit rates. This technique uses switching between JPEG compression technique at high bit rates and a novel Adaptive Lossy Image Compression (ALIC) technique at low bit rates. ALIC is proposed to overcome the drawbacks of JPEG technique at low bitrates. The performance of the proposed technique is analysed at low and high bit rates on both grey and colour images. Performances of both JPEG and ALIC techniques are analysed and compared. The experimental results reveal that the proposed ALIC technique achieves better CR with acceptable SNR in comparison with JPEG technique. Also, the resultant CR of ALIC technique can be considerably increased with a slight decrease of its PSNR. This decrease in PSNR does not result in a noticeable visual degradation of the compressed image. On the other hand, increasing the CR of JPEG technique results in a noticeable visual degradation due to the appearance of blocking effect in the reconstructed image. Thus, it is greatly recommended to use ALIC technique in the applications that require high CR with stable PSNR. ALIC is a general purpose technique that can be applied, not only on images, but also on any data source which uses Huffman coding to achieve better CR. Therefore, it is suitable for compression of text, image and video.

Recently, the use of large volumes of image data in many applications like internet has been increasing rapidly. So, to make an effective use of storage space and also bandwidth of the network, image compression is required. We have two kinds of image compression - one is lossy and other is lossless image compression. Lossy image compression produces a compressed image where quality of the image is maintained with some data loss. Lossy compression is widely used compared to lossless compression. Here, three lossy image compression techniques - Discrete Cosine Transform(DCT), Singular Value Decomposition (SVD) and Discrete Wavelet Transform(DWT) are used to perform image compression. These techniques are compared using some performance measures such as Peak Signal-to- Noise Ratio(PSNR), Compression Ratio(CR), Structural Similarity Index Measure(SSIM) and Mean Square Error(MSE).

Over the period of time, the measure of data that is dealt by machines has increased rapidly. Consequently the storage space or memory required to store the digital image component of multimedia systems has become a significant issue. Therefore; image compression addresses this issue by reducing the measure of data required to present a digital image. Image can be compressed by Lossless or Lossy compression technique. In this paper, we are analyzing the lossy image compression technique. And, the type of lossy compression technique analyzed is ‘Transform Coding’. In transform coding, we analyze Hyper Analytic wavelet transform (HWT) and Huffman coding algorithm on FPGA. The architecture for HWT is designed using Verilog-HDL language and synthesized on Xilinx ISE tool such that performance, power consumption and area utilized are analyzed. The power consumed by the designed system is evaluated by the Xpower analysis tool, which yields 345mW at 100MHz clock frequency

Lossy image compression using singular value decomposition and wavelet difference reduction

In this paper, we propose a novel joint image compression and encryption technique where an annoying image can still be recovered even without the encryption key. Our work is based on JPEG standard. By embedding encryption algorithm at the transformation stage, we realize image encryption and compression together with controllable image quality. Instead of using the 8×8 discrete cosine transform (DCT) alone for transformation, we develop new orthogonal transforms by introducing sign-flips into the butterflies of DCT's flow-graph structure, and then employ them alternatively in JPEG's transformation stage according to a secret key. By carefully selecting the butterflies for sign-flipping, we can control the visual quality of the encrypted images. Finally, a detailed security analysis of our proposed encryption algorithm is presented to show its resistance to various attacks, such as cryptographic attack, replacement attack and statistical model-based attack.

Well-known optical image encryption technique double random phase encoding scheme has been shown vulnerable to basic attacks. Thereafter triple random phase encoding scheme is proposed to endure basic attacks. Recently, triple random phase encoding scheme is also shown vulnerable by using deep learning based attack. In this paper, an image encryption scheme based on triple random phase encoding with chaotic Henon map in Fourier domain is proposed. Henon map is used to strengthen the image encryption scheme. Henon map has two parameters and two initial conditions which are highly sensitive to its original value. Experiments were carried out on grayscale images to validate the proposed scheme. Statistical attacks such as information entropy, histogram and 3-D plot analysis are successfully endured by the scheme. Performances against noise and occlusion attacks show the robustness of the scheme. The key sensitivity results indicate that the proposed scheme is highly secure.