Next Generation Image Compression And Manipulation Using CREW

<title>Evaluation of color-embedded wavelet image compression techniques</title>

“…Thus, these components are commonly subsampled to remove redundancy as in the JPEG and MPEG standards. A problem with the transform described above is that it is not reversible † [3,4]. A reversible transformation is desired for lossless compression.…”

“…The wavelet transform has been widely used in image and video compression since it allows localization in both the space and frequency domains [1,2,4,11,7,19]. Typically an image is decomposed into a hierarchy of frequency subbands that are processed in an independent manner.…”

“…One of the advantages of this transformation is that it reduces the psychovisual redundancy in an image [4]. It has been shown that the human visual system is relatively insensitive to the high frequency content of the chrominance components [14].…”

“…This permits the decoder to truncate the bit stream once a desired data rate or distortion has been achieved. Transform coding is commonly utilized in embedded coding, with the wavelet transform [1,2,3,4,5,6,7] being the often used transform, although other alternative transforms such as the Laplacian pyramid [8] and the discrete cosine transform [9] have been used. The greatest distortion reduction is achieved if the transform coefficients with the largest magnitudes are coded initially and with high precision.…”

Image Compression Using Spline Based Wavelet Transforms

“…Following these concepts, the data object emerging from the quantizer is first partitioned into different subsources. Parts of cor-relations within and between different subsources are then captured by aggregating homogeneous elements into data structures like run-length codes or zerotrees), EQ [28], (Image Coding Based on Mixture Modeling of Wavelet Coefficients and a Fast Estimation-Quantization Framework introduces an image compression paradigm that combines compression efficiency with speed, and is based on an independent "infinite" mixture model which accurately captures the space-frequency characterization of the wavelet image representation), Morphological Representation of Wavelet Data (MRWD) [35], (presents both an experimental study of the statistics of wavelet data, as well as the design of two different morphology-based coding algorithms, that make use of these statistics), SLCCA [12], (Significance-Linked Connected Component Analysis for Wavelet Image Coding, is a wavelet image coder which extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields), Context Based (C/B) [16], (Context-Based Entropy Coding for Lossy Wavelet Image Compression which is an adaptive image coding algorithm based on backward adaptive quantization-classification techniques using a simple uniform scalar quantizer to quantize the image subbands), OC [26], Optimal Classification in Subband Coding of Images investigates various classification techniques, applied to subband coding of images, as a way of exploiting the non-stationary nature of image subbands), CREW [13], EPWIC [14], EBCOT [38], (Scalable Image Compression which is based on independent Embedded Block Coding with Optimized Truncation of the embedded bit-streams, which identifies some of the major contributions of the algorithm. The EBCOT algorithm [38] uses a wavelet transform to generate the subband coefficients which are then quantized and coded.…”