A Survey on Data Hiding and Compression Schemes

Abstract

Data hiding has a vital role to play in information security. Using a data hiding technique, secret information is hidden into cover digital content. Compression techniques and data hiding techniques received much less attention from the research community and from industry than cryptography. In past research many data hiding and image compression techniques has been developed, it works independent module in both sender and server side. These method cause low efficiency, less security and also the low bit rate scheme requires more time to encode. Both image compression and data hiding (EJDHC) using residual codebooks with side match vector quantization (SMVQ). In this survey is to discuss on data hiding schemas and compression techniques such as JPEG, JPEG 2000, vector quantization, VQ compression and SMVQ. KEYWORDS:Data hiding, image compression, side match vector quantization I.INTRODUCTIONData hiding is a process to hide data into cover media. The data hiding process links two sets of data and a set of the embedded data and another set of the cover media data. The relationship between these two sets of data characterizes different applications. In authentication phase embedded data are closely related to the cover media. High Data hiding in images [1] 8-bit grayscale images are selected as the cover media called as cover images. Cover images with the secret messages embedded in the images. For data hiding methods, the image quality refers to the quality of the images. Most of the hiding techniques is based on manipulating the least-signi7cant-bit (LSB) planes by directly replacing the LSBs of the cover image with the message bits. LSB methods typically achieve high capacity. Another technique introduced in [2] to improve image quality a local pixel adjustment process an optimal substitution matrix for the embedding of the secret messages. Image compression purpose is data reduction that can reduce the quantity of data. Image compression resolved the problem of reducing the amount of data required to represent an image. It is a process planned to yield a compact representation of an image by reducing the image storage. In Image compression has two ways lossy and lossless schemes Lossy schemes provide much higher compression ratios than lossless schemes. Lossy schemes are widely used since the quality of the reconstructed images is adequate for most applications. Lossless compression is used for medical imaging and arts. Data compression is being used for applications in image processing. In fact, very surprising we can see that many authors use data compression techniques for classification and / or segmentation images, filter or denoising image, artifacts detection in images, detecting altered images, etc.Data hiding [6] also can be classified into three domains, namely, spatial, transformative, and compression. In the spatial domain each pixel in the cover image is modified to hide the secret information. In the transformative domain the cover image is transformed into coefficients using well-known...