In this article, a new vector quantization (VQ) scheme called adaptive classified side-match finite-state vector quantization (ACSMVQ) is presented. This technique takes advantage of classified vector quantization (CV...
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In this article, a new vector quantization (VQ) scheme called adaptive classified side-match finite-state vector quantization (ACSMVQ) is presented. This technique takes advantage of classified vector quantization (CVQ) and side-match vector quantization (SMVQ) so that the compression efficiency is much more attractive than traditional VQ techniques. In ACSMVQ, image blocks are classified into two main classes: edge blocks and non-edge blocks. For improving image quality, non-edge and edge blocks are further reclassified into different classes based on the properties of neighbouring blocks. Kirsh operators are utilized for the detection of edge properties within an image block. In order to synchronize the states in the encoder and decoder, the quantized upper and left blocks are exploited for classifying the input block both in the encoder and decoder. Therefore, no class index needs to be transmitted to the decoder. Moreover, the compression ratio can be further improved by applying small state code book. As shown in the experiment results, the average improvement of ACSMVQ over ordinary SMVQ is up to 2.7 dB at nearly the same bit-rate. Moreover, in comparison with ordinary VQ, the average improvement can be up to 4.24 dB at the same bit-rate.
This paper proposes a new ADPCM method for image coding called directional ADPCM which can remove more redundancy from the image signals than the conventional ADPCM. The conventional ADPCM calculates the two-dimension...
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ISBN:
(纸本)0819416223;9780819416223
This paper proposes a new ADPCM method for image coding called directional ADPCM which can remove more redundancy from the image signals than the conventional ADPCM. The conventional ADPCM calculates the two-dimensional prediction coefficients by using the correlation functions and solving the Yule-Walker equation. Actually, the quantities of correlation functions are replaced by the sample averages. Therefore, this solution will not be optimum. Our directional ADPCM utilizes the directional filters to obtain the energy distribution in four directions and then determines the four directional prediction coefficients. All the directional filters are designed by using the singular value decomposition (SVD) method and the two-dimensional Hilbert transform technique. In the experiments, we illustrate that the M.S.E. for the directional ADPCM is less than that of the conventional ADPCM.
A two-dimensional interpolative model for coding of images is presented. It is shown that this model leads to three different coding algorithms, each algorithm defining a specific transmitter-receiver architecture. Si...
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A two-dimensional interpolative model for coding of images is presented. It is shown that this model leads to three different coding algorithms, each algorithm defining a specific transmitter-receiver architecture. Simulation examples on a 255 × 255 image are given. Computational aspects and possible extensions are discussed.
A new source encoder called the adaptive block quantizer is proposed for coding data sources that emit a sequence of correlated real numbers with known first- and second-order statistics, Blocks of source output symbo...
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A new source encoder called the adaptive block quantizer is proposed for coding data sources that emit a sequence of correlated real numbers with known first- and second-order statistics, Blocks of source output symbols are first classified and then block quantized in a manner that depends on their classification. The system is optimized relative to both the mean square error and the subjective quality of the reconstructed data for a certain class of pictorial data, and the resulting system performance demonstrated. Some interesting relationships between mean square error and subjective picture quality are presented.
The discrete cosine-III transform (DC3T) is the same as the discrete symmetric cosine transform (DSCT) with a specific preprocessing of input data. It has less computational complexity than the discrete cosine transfo...
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The discrete cosine-III transform (DC3T) is the same as the discrete symmetric cosine transform (DSCT) with a specific preprocessing of input data. It has less computational complexity than the discrete cosine transform (DCT) in terms of multiplications. The DC3T is also related to the DCT by a weighting matrix. The performance of the DC3T is compared to the DCT when compression is performed by adaptive coding. Experimental results show that a significant improvement in visual performance and mean square reconstruction error can be achieved over what is possible with the DCT. The better performance is attributed to the weighting of the DCT coefficients, which is indirectly achieved at reduced computational cost.
A new image coding technique based on the Multiscale Matching Pursuits (MMP) approach is presented. Using a predefined dictionary set, which consists of a limited amount of elements, the MMP approach can decompose/enc...
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A new image coding technique based on the Multiscale Matching Pursuits (MMP) approach is presented. Using a predefined dictionary set, which consists of a limited amount of elements, the MMP approach can decompose/encode images on different image scales and reconstruct/decode the image by the same dictionary. The MMP approach can be used to represent different scale image texture as well as the whole image. Instead of the pixel-based image representation, the MMP method represents the image texture as an index of a dictionary and thereby can encode the image with low data volume. Based on the MMP operation, the image content can be coded in an order from global to local and detail.
The main coding techniques for efficient electronic representation of grayscale images are reviewed. The emphasis is on understanding some of the reasons behind the development of different coding structures. It is sh...
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The main coding techniques for efficient electronic representation of grayscale images are reviewed. The emphasis is on understanding some of the reasons behind the development of different coding structures. It is shown that various (conscious or unconscious) models of the scene, the image, and the visual system have been and are still being used as the driving source for the construction of coding algorithms. Methods covered in this include delta modulation, predictive coding, transform coding, vector quantization, hybrid coding, subband coding, and semantic image coding. The last two schemes are covered in greater detail. Subband coding forms a bridge between traditional (waveform) concepts and perceptual coding. Semantic coding is an example of a class of new coding techniques based on (3-D) object modeling.
Directional intra prediction plays an important role in current state-of-the-art video coding standards. In directional prediction, neighbouring samples are projected along a specific direction to predict a block of s...
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Object boundaries detected by edge detection algorithms provide a rich, meaningful and sparse description of an image. In this study, we develop an image compression algorithm based on such a sparse description which ...
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ISBN:
(纸本)081941638X;9780819416384
Object boundaries detected by edge detection algorithms provide a rich, meaningful and sparse description of an image. In this study, we develop an image compression algorithm based on such a sparse description which is obtained by using weak membrane model of the image. In this approach, image is modelled as a collection of smooth regions separated by edge contours. This model allows us to determine edge contours, represented as line processes, by minimizing a nonconvex energy functional associated with a membrane, and to reconstruct the original image by using the same model. Thus despite the previous work where first edges are obtained by an edge detection algorithm based on convolution and then surface is reconstructed by using a completely different process such as interpolation, in our approach the same process is used for both detecting edges and reconstructing surfaces from them. We coded the line processes by using run length coding and the sparse data around line processes by using the entropy coding. We evaluate the performance of the algorithm qualitatively and quantitatively on various synthetic and real images, and show that good quality images can be obtained for moderate compression ratio like 5:1 while this ratio may reach up to 20:1 for some images.
In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the neces...
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In this paper, we propose a sparse overcomplete image approximation method based on the ideas of overcomplete log-Gabor wavelet, mean shift and energy concentration. The proposed approximation method selects the necessary wavelet coefficients with a mean shift based algorithm, and concentrates energy on the selected coefficients. It can sparsely approximate the original image, and converges faster than the existing local competition based method. Then, we propose a new compression scheme based on the above approximation method. The scheme has compression performance similar to JPEG 2000. The images decoded with the proposed compression scheme appear more pleasant to the human eyes than those with JPEG 2000.
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