In this paper, we describe a new lossless compression algorithm for color video sequences. Our approach is to exploit both spatial and temporal redundancies in the video sequence by developing a new adaptive algorithm...
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ISBN:
(纸本)0819452114
In this paper, we describe a new lossless compression algorithm for color video sequences. Our approach is to exploit both spatial and temporal redundancies in the video sequence by developing a new adaptive algorithm. By adaptive selection between spatial and temporal prediction we show that our scheme is better than state-of-the-art lossless compression algorithms.
Four-dimensional (4D) medical images, which are sequences of volume images over time, are represented by a great volume of information. One set of 4D medical images can easily take up hundreds of megabytes of storage ...
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Four-dimensional (4D) medical images, which are sequences of volume images over time, are represented by a great volume of information. One set of 4D medical images can easily take up hundreds of megabytes of storage volume. In this paper, we present a technique for compressing 4D medical images by combining motion compensation and lossless/near-lossless image encoding. Our technique incorporates a new fast 3D cube match algorithm that effectively exploits the redundancy between frames. The proposed scheme is validated by experiments on 4D cardiac images.
Lossless compression techniques are essential in archival and communication of large amounts of homogeneous data in radiological image databases. This paper exploits dependencies that exist between the pixel intensiti...
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ISBN:
(纸本)0819440868
Lossless compression techniques are essential in archival and communication of large amounts of homogeneous data in radiological image databases. This paper exploits dependencies that exist between the pixel intensities in three dimensions to improve compression for a set of similar medical images, These 3-D dependencies are systematically presented as histograms, plots of wavelet decomposition coefficients, feature vectors of wavelet decomposition coefficients, entropy and correlation. This 3-D dependency is called set redundancy for medical image sets. predictive coding is adapted to set redundancy and combined with integer wavelet transformations to improve compression. This set compression improvement is demonstrated with 3-D sets of magnetic resonance (MR) brain images.
We propose an application of the ridgelet transform to semantic objects which are produced by the watershed segmentation. The ridgelet transform is effective in representing line singularities; therefore, it is a powe...
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We propose an application of the ridgelet transform to semantic objects which are produced by the watershed segmentation. The ridgelet transform is effective in representing line singularities; therefore, it is a powerful tool for coding. Moreover, it has the advantageous property of rotating a block easily by shifting coefficients in ridgelet domain. Nevertheless, the rotating target is not a rectangular block but a real object. For that reason, we divide a picture into semantic objects using the watershed segmentation, and each object is converted by the ridgelet transform; then, we can rotate each object easily. predictive coding with this method is attempted. Simulations show that referencing object with rotation is very effective in prediction.
Natural, continuous tone images have a very important property of high correlation of adjacent pixels. This property is cleverly exploited in lossless image compression where, prior to the statistical modeling and ent...
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ISBN:
(纸本)9539676967
Natural, continuous tone images have a very important property of high correlation of adjacent pixels. This property is cleverly exploited in lossless image compression where, prior to the statistical modeling and entropy coding step, predictive coding as decorrelation toot is used. The use of prediction for current pixel also reduces the model cost of applied statistical model for entropy coding. Linear prediction, where predicted value is linear function of previously encoded pixels (causal template), has proven to give very good results as a decorrelation tool in lossless image compression. In this work we concetrate on adaptive linear predictors used in lossless image coding and propose a new linear prediction method.
Image-based rendering data sets, such as light fields, require efficient compression due to their large data size, but also easy random access when rendering from the data set. Efficient compression usually depends up...
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Image-based rendering data sets, such as light fields, require efficient compression due to their large data size, but also easy random access when rendering from the data set. Efficient compression usually depends upon prediction between images, which creates dependencies between them, conflicting with the requirement of having easy random access. In this paper we propose to eliminate prediction at the encoder by using Wyner-Ziv coding for compressing the light field images. The images are independently encoded by a Wyner-Ziv encoder. At the receiver, previously reconstructed images are used by the Wyner-Ziv decoder as side information to exploit similarities among images. Simulation results show significant compression performance gains compared to conventional independent image coding while maintaining random access capabilities.
Gradient adjusted prediction (GAP) is an efficient prediction method for lossless compression of video. By calculating its gradient in latitudinal and longitudinal orientation with the known context, the pixel value c...
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ISBN:
(纸本)0780386477
Gradient adjusted prediction (GAP) is an efficient prediction method for lossless compression of video. By calculating its gradient in latitudinal and longitudinal orientation with the known context, the pixel value can be well estimated and used for predictive coding. However, when realizing this method in a real-time compression system, it needs a large amount of memory space. The number of operations for each pixel is also large. In this paper, we introduce an optimized algorithm to realize GAP in a digital signal processor (DSP) named Analog Device Blackfin/spl trade/ DSP BF533. The GAP calculation is divided into two parts: gradient calculation and prediction calculation, and carefully designed to utilize the direct memory access (DMA) operation and parallel instruction provided by BF533. Worthless repetition and stalls in the DSP pipeline are avoided.
Image-based rendering data sets, such as light fields, require efficient compression due to their large data size, but also easy random access when rendering from or streaming the data set. Efficient compression usual...
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Image-based rendering data sets, such as light fields, require efficient compression due to their large data size, but also easy random access when rendering from or streaming the data set. Efficient compression usually depends upon prediction between images, which creates dependencies between them, making random access more difficult. Independent encoding of the images is less efficient but provides random access, and in many cases is superior to predictive coding when considering the rate-distortion performance for certain viewing trajectories of the light field. In this paper, we describe a method which can obtain the best of both worlds: random access to any image; and compression efficiency, through predictive coding. We store multiple representations of each light field image, and decode using the appropriate representation depending on the available images that are already decoded. To eliminate prediction mismatch between the representations, we employ the idea of "SP-frames" from video coding. This allows us to generate identical reconstruction images from all representations. Our experimental results show a significant improvement in terms of view-trajectory-dependent rate-distortion performance over independent coding of the images.
This paper presents adaptive arithmetic coding of prediction errors in lossless image compression. Generally, a probability distribution of the errors forms Laplacian distribution with zero mean, but the variance /spl...
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This paper presents adaptive arithmetic coding of prediction errors in lossless image compression. Generally, a probability distribution of the errors forms Laplacian distribution with zero mean, but the variance /spl sigma/ of the distribution may take different value at each local area in the image. The proposed encoder estimates the variance /spl sigma/ at every pixel to update the probability table. First, at a target pixel, the variance /spl sigma/ that maximizes the posterior probabilities of neighboring errors is calculated. Next, the error at the target pixel is encoded by arithmetic coding based on probability distribution with the variance /spl sigma/. Since this method calculates the probabilities from fewer neighboring errors, they respond to the rapid changes of image characteristic in narrow area. In this paper, the proposed method is compared with Lempel-Ziv, Huffman, static/adaptive arithmetic coding and JPEG arithmetic coding, and then compression ratios are discussed. On an average, it generates 5% smaller size of compressed data than the adaptive arithmetic method by JPEG.
We present a novel mode decision scheme with which a portion of the macroblocks' intra/inter coding mode is determined without performing the exhaustive spatial estimation and motion estimation searches. The codin...
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We present a novel mode decision scheme with which a portion of the macroblocks' intra/inter coding mode is determined without performing the exhaustive spatial estimation and motion estimation searches. The coding modes of such macroblocks are determined utilizing the correlation across spatially and temporally close macroblocks' prediction modes. By bypassing a large amount of spatial estimation and motion estimation searches, the scheme can reduce the computational complexity substantially. Simulation results show that the coding performance using the proposed mode decision scheme is perceptually as good as using traditional mode decision. The proposed scheme can be applied to any modern video codec that employs predictive coding, including H.263, MPEG-4, and H.264/AVC.
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