Quality Of Decompressed Image Research Articles

Classical vs. neural network-based PCA approaches for lossy image compression: Similarities and differences

The paper describes three lossy data compression techniques based on the principal component analysis (PCA), which are compared using the image compression task. The presented approach uses both classical PCA method based on the eigen-decomposition of the image data covariance matrix and two different neural network structures. The first neural structure is a two-layer feed-forward network with supervised learning acting as the so-called autoencoder, and the second one is a single-layered network with an unsupervised learning method commonly known as the generalized Hebbian algorithm. For each compression method mentioned above, the influence of the number of image segments (frames) and the number of eigenvalues/eigenvectors on the compression ratio and the image quality are examined using three different gray-scale test images. The paper also addresses a vital implementation issue regarding selecting appropriate data types to represent the compressed data. The paper’s main conclusion is that the classical PCA method outperforms its neural counterparts, both in terms of the decompressed image quality and the time required to perform the compression procedure. The positive aspect of using neural networks as a tool for PCA-based lossy data compression is that they do not require calculating the correlation matrix explicitly and thus can be used in online data acquisition schemes.

An Invalid Cloud Region Masking Method for Remote Sensing Image Compression

An invalid cloud region masking method based on local edge context is proposed for the compression of remote sensing images. Through analyzing the characteristics of various compression algorithms and taking the local edge information of the cloud region into account, the decompression quality is improved. First, according to the cloud mask information, labeling the connected cloud region, second, performing region growth on the labeled mask image, then differing the two mask images to obtain the local edge context, and finally different invalid cloud regions are filled with the average of the respective local edge context pixels. Using the image testing set generated from QuickBird and OrbView images, our method’s impact on six common remote sensing image compression algorithms is analyzed experimentally. The experimental results show that our masking method can improve the decompressed image quality when the compression ratio is certain. When the image quality is fixed, it can further reduce the compressed bitrate. For onboard application, our masking method can increase the onboard imaging time of the satellites, and eventually improve the onboard specifications of remote sensing satellites.

Image Forgery Detection Using Dct And Quantization Matrix Techniques

In today’s era the image has become useful for communication purpose. But due to the development of software and various techniques it is possible to change images in adding or removing essential feature from it without leaving a clue of real image. It is not easy for the common people to identify whether the image original or tampered. In order to avoid this problem, forgery detection came into existence. Detection of forgery refers to task of image processing to identify that the images are unique or tampered. Several techniques have been used in order to detect the forgeries from the forged image, but this issue has not yet solved. In order to solve these issues we have used Discrete Cosine Transformation (DCT) and quantization matrix techniques for identifying forged areas of image, where the quality of image is not reduced. The Discrete Cosine Transformation (DCT) is used in order for characterizing the overlapping blocks and quantization matrix is used to compress DCT values and gives both highly compressed and best decompressed image quality. Here we use block matching algorithm. This algorithm one of the most frequently used for detecting image which is duplicate. This proposed work also supports for different kinds of images such as JPEG, JPG or PNG of any size it can be either mxn or nxn.

On holographic transform compression of images

Lossy transform compression of images is successful and widespread. The JPEG standard uses the discrete cosine transform on blocks of the image and a bit allocation process that takes advantage of the uneven energy distribution in the transform domain. For most images, 10:1 compression ratios can be achieved with no visible degradations. However, suppose that multiple versions of the compressed image exist in a distributed environment such as the internet, and several of them could be made available upon request. The classical approach would provide no improvement in the image quality if more than one version of the compressed image became available. In this paper, we propose a method, based on multiple description scalar quantization, that yields decompressed image quality that improves with the number of compressed versions available. © 2001 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 11, 292–314, 2000

Region-based fractal image compression using heuristic search

Presents work carried out on fractal (or attractor) image compression. The approach relies on the assumption that image redundancy can be efficiently exploited through self-transformability. The algorithms described utilize a novel region-based partition of the image that greatly increases the compression ratios achieved over traditional block-based partitionings. Due to the large search spaces involved, heuristic algorithms are used to construct these region-based transformations. Results for three different heuristic algorithms are given. The results show that the region-based system achieves almost double the compression ratio of the simple block-based system at a similar decompressed image quality. For the Lena image, compression ratios of 41:1 can be achieved at a PSNR of 26.56 dB.