IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Designing an efficient image encryption then-compression system via prediction error clustering and random permutation
- 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:[email protected] Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation ABSTRACT: In many practical scenarios, image encryption has to be conducted prior to image compression. This has led to the problem of how to design a pair of image encryption and compression algorithms such that compressing the encrypted images can still be efficiently performed. In this paper, we design a highly efficient image encryption-then-compression (ETC) system, where both lossless and lossy compression are considered. The proposed image encryption scheme operated in the prediction error domain is shown to be able to provide a reasonably high level of security. We also demonstrate that an arithmetic coding-based approach can be exploited to efficiently compress the encrypted images. More notably, the proposed compression approach applied to encrypted images is only slightly worse, in terms of compression efficiency, than the state-of-the-art lossless/lossy
- 2. image coders, which take original, unencrypted images as inputs. In contrast, most of the existing ETC solutions induce significant penalty on the compression efficiency. EXISTING SYSTEM: Compression-then-Encryption (CTE) paradigm meets the requirements in many secure transmission scenarios, the order of applying the compression and encryption needs to be reversed in some other situations. As the content owner, Alice is always interested in protecting the privacy of the image data through encryption. Nevertheless, Alice has no incentive to compress her data, and hence, will not use her limited computational resources to run a compression algorithm before encrypting the data. This is especially true when Alice uses a resource-deprived mobile device. In contrast, the channel provider Charlie has an overriding interest in compressing all the network traffic so as to maximize the network utilization. It is therefore much desired if the compression task can be delegated by Charlie, who typically has abundant computational resources. DISADVANTAGES OF EXISTING SYSTEM: the existing ETC systems still fall significantly short in the compression performance, compared with the state-of-the-art lossless/lossy image and video coders that require unencrypted inputs. The primary focus of this work is on the practical design of a pair of image encryption and compression schemes, in such a way that compressing the encrypted images is almost equally efficient as compressing their original, unencrypted counterparts.
- 3. PROPOSED SYSTEM: To achieve higher compression ratios, lossy compression of encrypted data was also studied . Zhang et. Al proposed a scalable lossy coding framework of encrypted images via a multi-resolution construction. In a compressive sensing (CS) mechanism was utilized to compress encrypted images resulted from linear encryption. A modified basis pursuit algorithm can then be applied to estimate the original image from the compressed and encrypted data. Another CS-based approach for encrypting compressed images was reported. Furthermore, Zhang designed an image encryption scheme via pixel-domain permutation, and demonstrated that the encrypted file can be efficiently compressed by discarding the excessively rough and fine information of coefficients in the transform domain. Recently, Zhang et. al suggested a new compression approach for encrypted images through multi-layer decomposition. Extensions to blind compression of encrypted videos were developed ADVANTAGES OF PROPOSED SYSTEM: Highly efficient compression of the encrypted data has then been realized by a context-adaptive arithmetic coding approach. Within the proposed framework, the image encryption has been achieved via prediction error clustering and random permutation.
- 4. SYSTEM ARCHITECTURE: SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS: System : Pentium IV 2.4 GHz. Hard Disk : 40 GB. Floppy Drive : 1.44 Mb. Monitor : 15 VGA Colour. Mouse : Logitech. Ram : 512 Mb. SOFTWARE REQUIREMENTS: Operating system : Windows XP/7. Coding Language : MATLAB Tool : MATLAB R 2007B
- 5. REFERENCE: Jiantao Zhou, Member, IEEE, Xianming Liu, Member, IEEE, Oscar C. Au, Fellow, IEEE, and Yuan Yan Tang, Fellow, IEEE ,“Designing an Efficient Image Encryption-Then-Compression System via Prediction Error Clustering and Random Permutation”, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 9, NO. 1, JANUARY 2014.