Wavelet-compressed Images Research Articles

Analysis of Wavelet Compression and Seam Carving Using the Hausdorf Distance

In this paper image quality of two types of compression methods, wavelet based and seam carving based are investigated. A metric is introduced to compare the image quality under wavelet and seam carving schemes. Meyer, Coiflet 2 and Jpeg2000 wavelet based methods are used as the wavelet based methods. Hausdorf distance based metric (HDM) is proposed and used for the comparison of the two compression methods instead of model based matching techniques and correspondence-based matching techniques, because there is no pairing of points in the two sets being compared. In addition entropy based metric (EM) or peak signal to noise ration based metric (PSNRM) cannot be used to compare the two schemes as the seam carving tends to deform the objects. The wavelet compressed images with different compression percentages were analyzed with HDM and EM and it was observed that HDM follows the EM/PSNRM for wavelet based compression methods. Then HDM is used to compare the wavelet and seam carved images for different compression percentages. The initial results showed that HDM is better metric for comparing wavelet based and seam carved images.

Blind Assessment of Wavelet-Compressed Images Based On Subband Statistics of Natural Scenes

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

Restoration of wavelet-compressed images

We investigate the characteristics of compression noise in images compressed by scalar quantization of the data's wavelet transform coefficients. Such quantization noise is both experimentally and theoretically shown to be spatially varying in the pixel domain, with statistical correlations between the errors at the pixel locations. A quantization covariance matrix is presented that can find use in general restoration scenarios where the observed image or images have been compressed by scalar quantization of image wavelet coefficients. Deblurring is presented as an example use of the quantization model, which demonstrates the model's advantage over the common assumption of independent and identically distributed noise.

A computational kernel for fast and efficient compressed-domain calculations of wavelet subband energies

This paper develops algorithms for calculating commonly used energy measures directly from the data stream of wavelet-compressed images. Incorporating these techniques into an underlying computational kernel allows many image-processing tasks to be efficiently implemented in the compressed domain. Compared to traditional decompress-process methods, the proposed techniques offer significant memory savings and reduce the computational load of the system. Experimental results show that the calculated energy values are accurate and obtained with less strain on system resources. The potential savings achieved by incorporating the proposed kernel are illustrated in a texture classification example.

Multiscale image segmentation using wavelet-domain hidden Markov models

We introduce a new image texture segmentation algorithm, HMTseg, based on wavelets and the hidden Markov tree (HMT) model. The HMT is a tree-structured probabilistic graph that captures the statistical properties of the coefficients of the wavelet transform. Since the HMT is particularly well suited to images containing singularities (edges and ridges), it provides a good classifier for distinguishing between textures. Utilizing the inherent tree structure of the wavelet HMT and its fast training and likelihood computation algorithms, we perform texture classification at a range of different scales. We then fuse these multiscale classifications using a Bayesian probabilistic graph to obtain reliable final segmentations. Since HMTseg works on the wavelet transform of the image, it can directly segment wavelet-compressed images without the need for decompression into the space domain. We demonstrate the performance of HMTseg with synthetic, aerial photo, and document image segmentations.

Model-based edge reconstruction for low bit-rate wavelet-compressed images

At low bit rates, wavelet-based image coding is superior to most traditional block-based methods in terms of visibility and severity of coding artifacts in coded images. However, the compressed images still suffer from obvious distortions around sharp edges, which are perceptually objectionable. In order to improve the image quality for low bit-rate wavelet-based image coding, we proposed a model-based edge-reconstruction algorithm for recovering the lossy edges in coded images. Our approach applies a general model to represent varieties of edges existing in an image. Based on this model, the edge degradation process due to quantization errors of wavelet coefficients is analyzed with the characterization of two kinds of artifacts at edges. We develop two operations, model-based edge approximation and Gaussian smoothing, to reconstruct distorted edges by reducing both artifacts respectively. The proposed method is able to improve the image quality in terms of both visual perception and image fidelity (peak signal-to-noise ratio) for most images coded by wavelet-based methods at low bit-rates.

Seamless multiresolution display of portable wavelet-compressed images.

Image storage, display, and distribution have been difficult problems in radiology for many years. As improvements in technology have changed the nature of the storage and display media, demand for image portability, faster image acquisition, and flexible image distribution is driving the development of responsive systems. Technology, such as the wavelet-based multiresolution seamless image database (MrSID) portable image format (PIF), is enabling image management solutions that address the shifting "point-of-care." The MrSID PIF employs seamless, multiresolution technology, which allows the viewer to determine the size of the image to be viewed, as well as the position of the viewing area within the image dataset. In addition the MrSID PIF allows control of the compression ratio of decompressed images. This capability offers the advantage of very rapid image recall from storage devices and portability for rapid transmission and distribution using the internet or wide-area networks. For example, in teleradiology, the radiologist or other physician desiring to view images at a remote location has full flexibility in being able to choose a quick display of an overview image, a complete display of a full diagnostic quality image, or both without compromising communication bandwidth. The MrSID algorithm will satisfy Joint Photographic Experts Group (JPEG) 2000 standards, thereby being compatible with future versions of the Digital Imaging and Communications in Medicine (DICOM) standard for image data compression.

Subjective Image Quality Evaluation of Image Compression Techniques

Image compression reduces the amount of data in digital images and, therefore, allows efficient storage, processing, and transmission of pictorial information. However, compression algorithms can degrade image quality by introducing artifacts, which may be unacceptable for users' tasks. This work examined the subjective effects of JPEG and wavelet compression algorithms on a series of medical images. Six digitized chest images were processed by each algorithm at various compression levels. Twelve radiologists rated the perceived image quality of the compressed images relative to the corresponding uncompressed images, as well as rated the acceptability of the compressed images for diagnostic purposes. The results indicate that subjective image quality and acceptability decreased with increasing compression levels; however, all images remained acceptable for diagnostic purposes. At high compression ratios, JPEG compressed images were judged less acceptable for diagnostic purposes than the wavelet compressed images. These results contribute to emerging system design guidelines for digital imaging workstations.