An object wave is a suitable intermediate representation format for transmitting holographic application data because it is compatible with various playback devices. However, compressing the phase component of the obj...
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In this paper, we propose a wavelet-based image-coding scheme allowing lossless and lossy compression, simultaneously, Our two-layered approach utilizes the best of two worlds: it uses a highly performing wavelet-base...
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In this paper, we propose a wavelet-based image-coding scheme allowing lossless and lossy compression, simultaneously, Our two-layered approach utilizes the best of two worlds: it uses a highly performing wavelet-based or wavelet packet-based coding technique for lossy compression in the low bit range as a first stage. For the second (optional) stage, we extend the concept of reversible integer wavelet transforms to the more flexible class of adaptive reversible integer wavelet packet transforms which are based on the generation of a whole library of bases, from which the best representation for a given residue between the reconstructed lossy compressed image and the original image is chosen using a fast-search algorithm. We present experimental results demonstrating that our compression algorithm yields a rate-distortion performance similar or superior to the best currently published pure lossy still image-coding methods, At the same time, the lossless compression performance of our two-layered scheme is comparable to that of state-of-the-art pure losslessimage-coding schemes. Compared to other combined lossy/losslesscoding schemes such as the emerging JPEG-2000 still image-coding standard PSNR improvements up to 3 dB are achieved for a set of standard test images.
This paper presents a new scalable locally adaptive resolution lossless low-complexity (LAR-LLC) image codec. It is based on the LAR framework that is a multiresolution compression method supporting both lossy and los...
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This paper presents a new scalable locally adaptive resolution lossless low-complexity (LAR-LLC) image codec. It is based on the LAR framework that is a multiresolution compression method supporting both lossy and losslesscoding. To achieve an efficient low-complexity solution, each processing stage of the LAR is modified. For the first step, consisting of a pyramidal decomposition, a new reversible transform called hierarchical diagonal S transform (HD-ST) is proposed. The HD-ST operates on sets of data pairs, requiring only shift and add/sub operations. The second step performs the prediction of the transformed coefficients. The prediction scheme considers both inter-and intra-level information, and involves fixed weights. Then, a classification process is introduced to separate prediction errors into subclasses, using a context modeling approach. Finally, each subclass is coded by the Huffman coding algorithm. The results of the lossless compression experiments showed that LAR-LLC achieves the same compression performance as JPEG2000 with a lower complexity.
Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploit...
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Significant lossless compression results of color map images have been obtained by dividing the color maps into layers and by compressing the binary layers separately using an optimized context tree model that exploits interlayer dependencies. Even though the use of a binary alphabet simplifies the context tree construction and exploits spatial dependencies efficiently, it is expected that an equivalent or better result would be obtained by operating directly on the color image without layer separation. In this paper, we extend the previous context-tree-based method to operate on color values instead of binary layers. We first generate an n-ary context tree by constructing a complete tree up to a predefined depth, and then prune out nodes that do not provide compression improvements. Experiments show that the proposed method outperforms existing methods for a large set of different color map images.
Multi-spectral and hyperspectral image data payloads have large size and may be challenging to download from remote sensors. To alleviate this problem, such images can be effectively compressed using specially designe...
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Multi-spectral and hyperspectral image data payloads have large size and may be challenging to download from remote sensors. To alleviate this problem, such images can be effectively compressed using specially designed algorithms. The new CCSDS-123 standard has been developed to address onboard losslesscoding of multi-spectral and hyperspectral images. The standard is based on the fast lossless algorithm, which is composed of a causal context-based prediction stage and an entropy-coding stage that utilizes Golomb power-of-two codes. Several parts of each of these two stages have adjustable parameters. CCSDS-123 provides satisfactory performance for a wide set of imagery acquired by various sensors;but end-users of a CCSDS-123 implementation may require assistance to select a suitable combination of parameters for a specific application scenario. To assist end-users, this paper investigates the performance of CCSDS-123 under different parameter combinations and addresses the selection of an adequate combination given a specific sensor. Experimental results suggest that prediction parameters have a greater impact on the compression performance than entropy-coding parameters. (C) 2013 Society of Photo-Optical Instrumentation Engineers (SPIE)
A new lossless intra coding method based on sample-by-sample differential pulse code modulation (DPCM) is presented as an enhancement of the H.264[MPEG-4 AVC standard. The H.264/AVC design includes a multidirectional ...
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A new lossless intra coding method based on sample-by-sample differential pulse code modulation (DPCM) is presented as an enhancement of the H.264[MPEG-4 AVC standard. The H.264/AVC design includes a multidirectional spatial prediction method to reduce spatial redundancy by using neighboring samples as a prediction for the samples in a block of data to be encoded. In the new lossless;intra coding method, the spatial prediction is performed based on samplewise DPCM instead of in the block-based manner used in the current H.264/AVC standard, while the block structure is retained for the residual difference entropy coding process. We show that the new method, based on samplewise DPCM, does not have a major complexity penalty, despite its apparent pipeline dependencies. Experiments show that the new lossless intra coding method reduces the bit rate by approximately 12% in comparison with the lossless;intra coding method previously included in the H.264/AVC standard. As a result, the new method is currently being adopted into the H.264/AVC standard in a new enhancement project.
In JPEG-LS, simple edge detection techniques are used in determining the predictive value of each pixel. These techniques only detect horizontal/vertical edges and have only been optimized for the prediction of pixels...
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In JPEG-LS, simple edge detection techniques are used in determining the predictive value of each pixel. These techniques only detect horizontal/vertical edges and have only been optimized for the prediction of pixels in the locality of such edges. Thus, JPEG-LS produces large prediction errors in the locality of diagonal edges. We propose a low complexity technique that accurately detects diagonal edges and efficiently predicts pixels, based on the information available within the standard predictive template of JPEG-LS. We show that the proposed technique outperforms JPEG-LS in terms of predicted mean squared error, by margins of up to 15%. (C) 2003 Elsevier B.V. All rights reserved.
We construct a theory of binary wavelet decompositions of finite binary images, The new binary wavelet transform uses simple modulo-2 operations. It shares many of the important characteristics of the real wavelet tra...
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We construct a theory of binary wavelet decompositions of finite binary images, The new binary wavelet transform uses simple modulo-2 operations. It shares many of the important characteristics of the real wavelet transform, In particular, it yields an output similar to the thresholded output of a real wavelet transform operating on the underlying binary image. We begin by introducing a new binary field transform to use as an alternative to the discrete Fourier transform over GF(2). The corresponding concept of sequence spectra over GF(2) is defined. Using this transform, a theory of binary wavelets is developed in terms of two-band perfect reconstruction filter banks in GF(2). By generalizing the corresponding real field constraints of bandwidth, vanishing moments, and spectral content in the filters, we construct a perfect reconstruction wavelet decomposition, We also demonstrate the potential use of the binary wavelet decomposition in lossless image coding.
This paper presents a new three-dimensional (3-D) wavelet-based scalable losslesscoding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectivene...
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This paper presents a new three-dimensional (3-D) wavelet-based scalable losslesscoding scheme for compression of volumetric medical images. Aiming to improve the productivity of radiologists and the cost-effectiveness of the system, we strive to achieve high decoder throughput, random access to coded data volume, progressive transmission, and high compression ratio in a balanced design approach. These desirable functionalities are realized by a modified 3-D dyadic wavelet transform tailored to volumetric medical images and an optimized Rice code of very low complexity.
This paper presents M-channel (M = 2(N), N is an element of N, N >= 1) multiplierless liftingbased (ML-) fast X transforms (FXTs), where X = F (Fourier), C (cosine), S (sine), and H (Hartley), i. e., FFT, FCT, FST,...
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This paper presents M-channel (M = 2(N), N is an element of N, N >= 1) multiplierless liftingbased (ML-) fast X transforms (FXTs), where X = F (Fourier), C (cosine), S (sine), and H (Hartley), i. e., FFT, FCT, FST, and FHT, derived from FHT factorization as way of lowering the cost of signal (image) processing. The basic forms of ML-FXTs are described. Then, they are customized for efficient image processing. The customized ML-FFT has a real-valued calculation followed by a complex-valued one. The ML-FCT customization for a block size of 8, which is a typical size for imagecoding, further reduces computational costs. We produce two customized ML-FCTs for lossy and lossless image coding. Numerical simulations show that ML-FFT and ML-FCTs perform comparably to the conventional methods in spite of having fewer operations.
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