In this paper various soft computing algorithms have been applied for image coding. One of the main common methods to compress images is to code them through vector quantization (VQ) techniques. The principle of the V...
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ISBN:
(纸本)0780388402
In this paper various soft computing algorithms have been applied for image coding. One of the main common methods to compress images is to code them through vector quantization (VQ) techniques. The principle of the VQ techniques is simple. At first, the image is split into square blocks of pixels, for example 4 x 4 or 8 x 8;each block is considered as a vector in a 16- or 64-dimensional space, respectively. Second, a limited number of vectors (codewords) in this space is selected in order to aproximate as much as possible the distribution of the initial vectors extracted from the image. Neural network based schemes such as MLP based non-orthogonal transform, nonlinear predictive coding, K-L transform and Kohonen SOM have been selected to achieve this goal. In this sense, this work presents the state of the art in image coding using neural networks.
In this article we evaluate the use of predictive coding for compression of SAR phase history data. We first show that the data are mainly correlated along range lines. Then, we exploit this result to define a new DPC...
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In this article we evaluate the use of predictive coding for compression of SAR phase history data. We first show that the data are mainly correlated along range lines. Then, we exploit this result to define a new DPCM-based compression algorithm named RDPCM-BAQ. The performance of this algorithm is compared with that of BAQ on SIR-C/X-SAR data, showing a significant improvement in signal-to-noise ratio of up to 2 dB with respect to BAQ.
In this paper various soft computing algorithms have been applied for image coding. One of the main common methods to compress images is to code them through vector quantization (VQ) techniques. The principle of the V...
详细信息
In this paper various soft computing algorithms have been applied for image coding. One of the main common methods to compress images is to code them through vector quantization (VQ) techniques. The principle of the VQ techniques is simple. At first, the image is split into square blocks of pixels, for example 4/spl times/4 or 8/spl times/8; each block is considered as a vector in a 16- or 64-dimensional space, respectively. Second, a limited number of vectors (codewords) in this space is selected in order to approximate as much as possible the distribution of the initial vectors extracted from the image. Neural network based schemes such as MLP based non-orthogonal transform, nonlinear predictive coding, K-L transform and Kohonen SOM have been selected to achieve this goal. In this sense, this work presents the state of the art in image coding using neural networks.
Scalable shape encoding is an important requirement of highly scalable object-based video coding. In this paper, a new scalable vertex-based shape coding scheme is proposed that uses temporal prediction. During shape ...
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Scalable shape encoding is an important requirement of highly scalable object-based video coding. In this paper, a new scalable vertex-based shape coding scheme is proposed that uses temporal prediction. During shape coding of a video object, the object shape in the first frame is scalable intra-coded using the method described in M. Hu et al., (2004). For scalable shape coding of subsequent frames, temporal prediction is conducted during the coding of coarser layers. Contour matching in the curvature scale space domain is conducted in order to get higher matching accuracy. Experimental results show that the proposed scalable shape coding scheme can achieve better R-D performance than existing predictive shape coding methods and CAE in MPEG-4.
This paper presents a new compression algorithm for medical images. The present algorithm is based on interframe predictive coding of images using wavelet transform for motion compensation. Simulation results show tha...
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ISBN:
(纸本)0780388127
This paper presents a new compression algorithm for medical images. The present algorithm is based on interframe predictive coding of images using wavelet transform for motion compensation. Simulation results show that the new algorithm has better performance than some existing algorithms. Compared with the 2-D wavelet compression, the new algorithm has a slightly higher compression ratio for CT images and the peak signal to noise ratio (PSNR) is increased for 3 dB. Compared with the DCT for motion compensation compression algorithm, the PSNR is increased for 5 dB using the new algorithm at the same compression ratio of 15.
We consider the bit allocation problem in multiview video coding (MVC). A dependent coding technique using trellis expansion and the Viterbi algorithm (VA) is proposed, which takes into account dependencies across tim...
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We consider the bit allocation problem in multiview video coding (MVC). A dependent coding technique using trellis expansion and the Viterbi algorithm (VA) is proposed, which takes into account dependencies across time and views. We note that, typically, optimal quantizer choices have the following properties: i) quantization choices tend to be similar for frames that are consecutive (in time or in view), ii) better quantization tends to be used for frames closer to the root of the dependency tree. We propose a search algorithm to speed up the optimization of quantization choices. Our results indicate significant gains can be achieved by an appropriate selection of bit allocation across frames.
In this paper, we present three novel lossless compression approaches for gray-scale continuous tone natural image. Our methods enhance the median edge detector (MED), which is the core part of JPED-LS algorithm, by r...
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In this paper, we present three novel lossless compression approaches for gray-scale continuous tone natural image. Our methods enhance the median edge detector (MED), which is the core part of JPED-LS algorithm, by reducing the entropy of the prediction error via adaptive regression. These modified predictors improve the prediction accuracy by reducing the negative effect due to MED's oversimplified edge orientation detection. The experimental results show that our approaches achieve evidently better performance than MED with only neglectable increasing of computational complexity and without introduce extra pixels into the causal template
In this paper we present a new method for image coding that is able to achieve good results over a wide range of image types. This work is based on the multidimensional multiscale parser (MMP) algorithm (M. de Carvalh...
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In this paper we present a new method for image coding that is able to achieve good results over a wide range of image types. This work is based on the multidimensional multiscale parser (MMP) algorithm (M. de Carvalho et al., 2002), allied with an intra frame image predictive coding scheme. MMP has been shown to have, for a large class of image data, including texts, graphics, mixed images and textures, a compression efficiency comparable (and, in several cases, well above) to the one of state-of-the-art encoders. However, for smooth grayscale images, its performance lags behind the one of wavelet-based encoders, as JPEG2000. In this paper we propose a novel encoder using MMP with intra predictive coding, similar to the one used in the H.264/AVC video coding standard. Experimental results show that this method closes the performance gap to JPEG-2000 for smooth images, with PSNR gains of up to 1.5 dB. Yet, it maintains the excellent performance level of the MMP for other types of image data, as text, graphics and compound images, lending it a useful universal character.
Compression efficiency and bitrate scalability are among the key factors in video coding. The paper introduces novel sub-sequence coding techniques for temporal scalability. The presented coding schemes provide a wide...
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Compression efficiency and bitrate scalability are among the key factors in video coding. The paper introduces novel sub-sequence coding techniques for temporal scalability. The presented coding schemes provide a wider range for bitrate scaling than conventional temporal scalability methods and maintain high coding efficiency at the same time. The proposed sub-sequence techniques are adopted into the latest video coding standard, H.264, making it easy to identify sub-sequences and possible to discard them intentionally. As shown by extensive simulations, a wide range of applications, from mobile messaging to consumer electronics, such as digital TV, can benefit from sub-sequences.
Two different aspects of distributed source coding is discussed. First, a previously developed distributed uniform scalar quantization method is improved by adopting non-uniform quantization. It is observed that compa...
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Two different aspects of distributed source coding is discussed. First, a previously developed distributed uniform scalar quantization method is improved by adopting non-uniform quantization. It is observed that compared to uniform quantization, the non-uniform scheme further approaches to the distortion-rate bound by 0.5 bit. Turning then to sources with memory, the tradeoff between exploitation of time and space correlation is exposed. It is shown by example that optimal transforms deviate from their traditional counterparts. Specifically, optimal transforms in the distributed framework do not necessarily fully decorrelate each sequence in time
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