Reconstruction of Image with Hebcot Compression Technique

The organization uses the cloud with completely different service models (SaaS, PaaS, IaaS) and preparation models (private, public, and hybrid). We extensively measure various security issues / concerns related to cloud computing, but these problems represent two major categories. Security issues confronted by cloud providers and security issues faced by customers. [1] In most cases, suppliers need to ensure infrastructure security, customer knowledge and application protection, and appropriate security measures for suppliers to protect their data.

Lossy compression technology has a small data loss, but it has a high compression ratio. The original image and the reconstructed image do not completely match. However, they are actually close to each other, and this difference is expressed as noise. In many applications, loss of data is unacceptable and must be lossless. In medical images compression using lossless techniques does not provide adequate advantages in terms of transmission and storage and compression using lossy techniques may lose important data necessary for diagnosis. In this paper, in order to obtain a high compressed image without losing data, we propose a combination of irreversible compression and lossless compression.

There are two types of compression: lossless and lossy. For lossless compression, the original image can be reconstructed from the compressed image, since no information is lost during compression. This is not true for lossy compression. With lossy compression, loss of data can not be restored. The lossy compression algorithm always has a better compression ratio (the ratio of the size of the compressed image to the original image) than lossless compression. However, this compression rate sacrifices quality and becomes more evident after the image is magnified. This significant degradation in quality or image distortion is called compression artifacts.

Lossless compression techniques provide a higher rate of compression than lossless compression techniques. However, some information is lost and data can not be rebuilt accurately. For some applications, precise reconstruction is not required. The lossy compression method is shown in Figure 2. The following subsections describe some lossy compression techniques. Wavelet analysis is considered an effective way to represent data (signals or images). Discrete wavelet transform (DWT) relies on filtering the image using high pass and low pass filters. In the first phase, we use two filters to downsample (maintain a constant index) row by row (horizontal) column and column by row for each sample of the filter output. This produces two DWT coefficients, each of which has a size of N × N / 2.