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Image compression frequently supports reduced storage requirement in a computer system, as well as enhancement of effective channel bandwidth in a communication system, by decreasing the source bit rate through reduction of source redundancy. The majority of image compression techniques emphasize pixel-level operations, such as matching rectangular or elliptical sampling blocks taken from the source data stream, with exemplars stored in a database (e.g., a codebook in vector quantization or VQ). Alternatively, one can represent a source block via transformation, coefficient quantization, and selection of coefficients deemed significant for source content approximation in the decompressed image. This approach, called transform coding (TC), has predominated for several decades in the signal and image processing communities. A further technique that has been employed is the deduction of affine relationships from source properties such as local self-similarity, which supports the construction of adaptive codebooks in a self-VQ paradigm that has been called <i>iterated function systems </i>(IFS). Although VQ, TC, and IFS based compression algorithms have enjoyed varying levels of success for different types of applications, bit rate requirements, and image quality constraints, few of these algorithms examine the higher-level spatial structure of an image, and fewer still exploit this structure to enhance compression ratio. In this paper, we discuss a fourth type of compression algorithm, called <i>object-based compression</i>, which is based on research in joint segmentaton and compression, as well as previous research in the extraction of sketch-like representations from digital imagery. Here, large image regions that correspond to contiguous recognizeable objects or parts of objects are segmented from the source, then represented compactly in the compressed image. Segmentation is facilitated by source properties such as size, shape, texture, statistical properties, and spectral signature. In particular, discussion addresses issues such as efficient boundary representation, variance assessment and representation, as well as a texture classification and replacement algorithms that can decrease compression overhead and increase reconstruction fidelity in the decompressed image. Contextual extraction of motion patterns in digital video sequences, using a frequency-domain pattern recognition technique based on interframe correlation, is described in a companion paper. This technique can also be extended to multidimensional image domains, to support joint spectral, spatial, and temporal compression.
6Simulation and testing of self-similar structures for soil particle-size distributions using iterated function systems
Simulation and testing of self-similar structures for soil particle-size distributions using iterated function systems
7Logarithmic quantisation of wavelet coefficients for improved texture classification performance
The coefficients of the wavelet transform have been widely used for texture analysis tasks, including segmentation, classification and synthesis. Second order statistics of such values have been shown to give excellent performance in these applications, and are typically calculated using co-occurrence matrices, which require quantisation of the coefficients. In this paper, we propose a non-linear quantisation function which is experimentally shown to better characterise textured images, and use this to formulate a new set of texture features, the wavelet log co-occurrence signatures.
Read more4An Algorithm for Improving the Quality of Compacted JPEG Image by Minimizes the Blocking Artifacts
The Block Transform Coded, JPEG- a lossy image compression format has been used to keep storage and bandwidth requirements of digital image at practical levels. However, JPEG compression schemes may exhibit unwanted image artifacts to appear - such as the ‘blocky’ artifact found in smooth/monotone areas of an image, caused by the coarse quantization of DCT coefficients. A number of image filtering approaches have been analyzed in literature incorporating value-averaging filters in order to smooth out the discontinuities that appear across DCT block boundaries. Although some of these approaches are able to decrease the severity of these unwanted artifacts to some extent, other approaches have certain limitations that cause excessive blurring to high-contrast edges in the image. The image deblocking algorithm presented in this paper aims to filter the blocked boundaries. This is accomplished by employing smoothening, detection of blocked edges and then filtering the difference between the pixels containing the blocked edge. The deblocking algorithm presented has been successful in reducing blocky artifacts in an image and therefore increases the subjective as well as objective quality of the reconstructed image.
Read more4Lower quantization coefficient and the F-conformal measure
Let $F=\{ f^{(i)} : 1\leq i\leq N\}$ be a family of Hölder continuous functions and let $\{\varphi_i : 1 \leq i\leq N\}$ be a conformal iterated function system. Lindsay and Mauldin's paper [Nonlinearity 15 (2002)] left an open question whether the lower
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