Multichannel image registration by feature-based information fusion

This paper proposes a novel nonrigid inter-subject multichannel image registration method which combines information from different modalities/channels to produce a unified joint registration. Multichannel images are created using co-registered multimodality images of the same subject to utilize information across modalities comprehensively. Contrary to the existing methods which combine the information at the image/intensity level, the proposed method uses feature-level information fusion method to spatio-adaptively combine the complementary information from different modalities that characterize different tissue types, through Gabor wavelets transformation and Independent Component Analysis (ICA), to produce a robust inter-subject registration. Experiments on both simulated and real multichannel images illustrate the applicability and robustness of the proposed registration method that combines information across modalities. This inter-subject registration is expected to pave the way for subsequent unified population-based multichannel studies.     

Multichannel blind iterative image restoration

Blind image deconvolution is required in many applications of microscopy imaging, remote sensing, and astronomical imaging. Unfortunately, in a single-channel framework, serious conceptual and numerical problems are often encountered. An eigenvector-based method (EVAM) has been proposed for a multichannel framework which determines perfectly convolution masks in a noise-free environment if channel disparity, called co-primeness, is satisfied (see Harikumar, G. and Bresler, Y., ibid., vol.8, no.2, p.202-19, 1999; Proc. ICIP 96, vol.3, p.97-100, 1996). We propose a novel iterative algorithm based on recent anisotropic denoising techniques of total variation and a Mumford-Shah functional with the EVAM restoration condition included. A linearization scheme of half-quadratic regularization together with a cell-centered finite difference discretization scheme is used in the algorithm and provides a unified approach to the solution of total variation or Mumford-Shah. The algorithm performs well even on very noisy images and does not require an exact estimation of mask orders. We demonstrate the capabilities of the algorithm on synthetic data. Finally, the algorithm is applied to defocused images taken with a digital camera and to data from astronomical ground-based observations of the Sun.     

Multichannel image processing by using the Rank M-type L-filter

In this paper, we introduce the Vector Rank M-type L (VRML)-filter to remove impulsive noise from color images and video sequences. The proposed filter uses the Median M-type (MM) and Ansari-Bradley-Siegel-Tukey M-type (AM) estimators into L-filter to provide robustness to proposed filtering scheme. We also introduce the use of impulsive noise detectors to improve the properties of noise suppression and detail preservation in the proposed filtering scheme in the case of low and high densities of impulsive noise. Simulation results indicate that the proposed filter consistently outperforms other color image filters by balancing the trade-off between noise suppression, detail preservation, and color retention.     

Reversible image compression bounded by noise

Reversible image compression rarely achieves compression ratios larger than about 3:1. An explanation of this limit is offered, which hinges upon the additive noise the sensor introduces into the image. Simple models of this noise allow lower bounds on the bit rate to be estimated from sensor noise parameters rather than from ensembles of typical images. The model predicts that an 8-b single-band image subject to noise with unit standard deviation can be compressed reversibly to no less than 2.0 b/pixel, equivalent to a maximum compression ratio of about 4:1. The model has been extended to multispectral imagery. The Airborne Visible and Infra Red Imaging Spectrometer (AVIRIS) is used as an example, as the noise in its 224 bands is well characterized. The model predicts a lower bound on the bit rate for the compressed data of about 5.5 b/pixel when a single codebook is used to encode all the bands. A separate codebook for each band (i.e., 224 codebooks) reduces this bound by 0.5 b/pixel to about 5.0 b/pixel, but 90% of this reduction is provided by only four codebooks. Empirical results corroborate these theoretical predictions     

Multichannel ECG data compression by multirate signal processingand transform domain coding techniques

A multilead electrocardiography (ECG) data compression method is presented. First, a linear transform is applied to the standard ECG lead signals, which are highly correlated with each other. In this way a set of uncorrelated transform domain signals is obtained. Then, the resulting transform domain signals are compressed using various coding methods, including multirate signal processing and transform domain coding techniques     

Multichannel ECG compression using multichannel adaptive vector quantization

Adaptive vector quantization (AVQ) is a recently proposed approach for electrocardiogram (ECG) compression. The adaptability of the approach can be used to control the quality of reconstructed signals. However, like most of other ECG compression methods, AVQ only deals with the single-channel ECG, and for the multichannel (MC) ECG, coding ECG signals on a channel by channel basis is not efficient, because the correlation across channels is not exploited. To exploit this correlation, an MC version of AVQ is proposed. In the proposed approach, the AVQ index from each channel is collected to form a new input vector. The vector is then vector quantized adaptively using one additional codebook called index codebook. Both the MIT/BIH database and a clinical Holter database are tested. The experimental results show that, for exactly the same quality of reconstructed signals, the MC-AVQ performs better than single-channel AVQ in terms of bit rate. A theoretical analysis supporting this result is also demonstrated in this paper. For the same and relatively good visual quality, the average compressed data rate/channel is reduced from 293.5 b/s using the single-channel AVQ to 238.2 b/s using the MC-AVQ in the MIT/BIH case.     

Multichannel transforms for signal/image processing

This paper presents a novel approach to the Fourier analysis of multichannel time series. Orthogonal matrix functions are introduced and are used in the definition of multichannel Fourier series of continuous-time periodic multichannel functions. Orthogonal transforms are proposed for discrete-time multichannel signals as well. It is proven that the orthogonal matrix functions are related to unitary transforms (e.g., discrete Hartley transform (DHT), Walsh-Hadamard transform), which are used for single-channel signal transformations. The discrete-time one-dimensional multichannel transforms proposed in this paper are related to two-dimensional single-channel transforms, notably to the discrete Fourier transform (DFT) and to the DHT. Therefore, fast algorithms for their computation can be easily constructed. Simulations on the use of discrete multichannel transforms on color image compression have also been performed.     

Medical image compression by sampling DCT coefficients

Advanced medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. Among the existing compression schemes, transform coding is one of the most effective strategies. Image data in the spatial domain is transformed into the spectral domain after the transformation to attain more compression gains. Based on the quantization strategy, coefficients of low amplitude in the transformed domain are discarded and significant coefficients are preserved to increase the compression ratio without inducing salient distortion. We use an adaptive sampling algorithm by calculating the difference area between correct points and predicted points to decide the significant coefficients. Recording or transmitting the significant coefficients instead of the whole coefficients achieves the goal of compression. On the decoder side, a linear equation is employed to reconstruct the coefficients between two sequent significant coefficients. Simulations are carried out to different medical images, which include sonogram, angiogram, computed tomography, and X-ray images     

Medical image compression by using three-dimensional wavelet transformation

This paper proposes a three-dimensional (3-D) medical image compression method for computed tomography (CT) and magnetic resonance (MR) that uses a separable nonuniform 3-D wavelet transform. The separable wavelet transform employs one filter bank within two-dimensional (2-D) slices and then a second filter bank on the slice direction. CT and MR image sets normally have different resolutions within a slice and between slices. The pixel distances within a slice are normally less than 1 mm and the distance between slices can vary from 1 mm to 10 mm. To find the best filter bank in the slice direction, the authors use the various filter banks in the slice direction and compare the compression results. The results from the 12 selected MR and CT image sets at various slice thickness show that the Haar transform in the slice direction gives the optimum performance for most image sets, except for a CT image set which has 1 mm slice distance. Compared with 2-D wavelet compression, compression ratios of the 3-D method are about 70% higher for CT and 35% higher for MR image sets at a peak signal to noise ratio (PSNR) of 50 dB, In general, the smaller the slice distance, the better the 3-D compression performance.     

Multichannel techniques in color image enhancement and modeling

We present novel multichannel methods in two target research areas. The first area is color image modeling. Multichannel AR models have been developed and applied to color texture segmentation and synthesis. The second area is color image equalization, which is performed on the three RGB channels simultaneously, using the joint PDF. Alternatively, equalization at the HSI domain is performed in order to avoid changes in digital image hue. A parallel algorithm is proposed for color image histogram calculation and equalization     

Multichannel photocells for image converters with color separation

The results of a study of photoelectric processes in photosensitive structures based on a multichannel vertically integrated p-n junction are presented. Optical radiation absorption in the space-charge region of a multichannel vertically integrated structure is studied.     

Error-tolerant SPIHT image compression

A new, error-tolerant, still image compression algorithm is described. An error-resilient adaptation of an existing zerotree wavelet coder is used to compress the image. Then, a variable-rate, concatenated error correction code is applied to protect the data further. Variable-rate protection exploits the progressive coding ability of the compressor, encouraging uncorrected errors to appear in the least significant portions of the bit-stream during decoding, which lessens their effect on the final image. The error-resilient organisation of the information inhibits the propagation of uncorrected errors, ensuring graceful degradation of the image over a wide range of bit error rates. Results for the popular 512×512 `Lena' image are presented, showing notable improvements over previously published algorithms     

TPG图像压缩技术

TPG(tiny portable graphic)是基于AVS2视频编码标准推出的图像压缩技术,该图片格式压缩效率明显高于JPG、PNG、GIF等其他传统格式.介绍了TPG图像编码技术的编码原理以及技术特点,对比了TPG图片格式与传统图片格式的压缩效率,结果显示,TPG图片格式具有明显编码增益.     

Space-frequency localized image compression

Subband and wavelet-based image compression can be viewed as frequency oriented techniques because each subimage in the decomposition is essentially a band-pass version of the original image. The authors suggest a space-frequency partition scheme to fully exploit the excellent localization properties of wavelets in both the spatial and frequency domains. Due to the relatively large number of blocks in this partition compared with traditional subband coders, the rate required for communicating the quantizer configurations must be taken into account. An iterative bit allocation algorithm is suggested that minimizes the mean square error given the overall rate for specifying the quantization configuration and for quantizing the wavelet coefficients. Images encoded using scalar quantization under this scheme show improvements in PSNR versus rate over traditional subband and wavelet-based methods.     

Gray-level-embedded lossless image compression

A level-embedded lossless compression method for continuous-tone still images is presented. Level (bit-plane) scalability is achieved by separating the image into two layers before compression and excellent compression performance is obtained by exploiting both spatial and inter-level correlations. A comparison of the proposed scheme with a number of scalable and non-scalable lossless image compression algorithms is performed to benchmark its performance. The results indicate that the level-embedded compression incurs only a small penalty in compression efficiency over non-scalable lossless compression, while offering the significant benefit of level-scalability.     

Weighted universal image compression

We describe a general coding strategy leading to a family of universal image compression systems designed to give good performance in applications where the statistics of the source to be compressed are not available at design time or vary over time or space. The basic approach considered uses a two-stage structure in which the single source code of traditional image compression systems is replaced with a family of codes designed to cover a large class of possible sources. To illustrate this approach, we consider the optimal design and use of two-stage codes containing collections of vector quantizers (weighted universal vector quantization), bit allocations for JPEG-style coding (weighted universal bit allocation), and transform codes (weighted universal transform coding). Further, we demonstrate the benefits to be gained from the inclusion of perceptual distortion measures and optimal parsing. The strategy yields two-stage codes that significantly outperform their single-stage predecessors. On a sequence of medical images, weighted universal vector quantization outperforms entropy coded vector quantization by over 9 dB. On the same data sequence, weighted universal bit allocation outperforms a JPEG-style code by over 2.5 dB. On a collection of mixed test and image data, weighted universal transform coding outperforms a single, data-optimized transform code (which gives performance almost identical to that of JPEG) by over 6 dB.     

Region-based fractal image compression

A fractal coder partitions an image into blocks that are coded via self-references to other parts of the image itself. We present a fractal coder that derives highly image-adaptive partitions and corresponding fractal codes in a time-efficient manner using a region-merging approach. The proposed merging strategy leads to improved rate-distortion performance compared to previously reported pure fractal coders, and it is faster than other state-of-the-art fractal coding methods.     

Multichannel ECG data compression based on multiscale principal component analysis

In this paper, multiscale principal component analysis (MSPCA) is proposed for multichannel electrocardiogram (MECG) data compression. In wavelet domain, principal components analysis (PCA) of multiscale multivariate matrices of multichannel signals helps reduce dimension and remove redundant information present in signals. The selection of principal components (PCs) is based on average fractional energy contribution of eigenvalue in a data matrix. Multichannel compression is implemented using uniform quantizer and entropy coding of PCA coefficients. The compressed signal quality is evaluated quantitatively using percentage root mean square difference (PRD), and wavelet energy-based diagnostic distortion (WEDD) measures. Using dataset from CSE multilead measurement library, multichannel compression ratio of 5.98:1 is found with PRD value 2.09% and the lowest WEDD value of 4.19%. Based on, gold standard subjective quality measure, the lowest mean opinion score error value of 5.56% is found.     

Color image compression by moment-preserving and block truncationcoding techniques

A new color image compression technique based on moment-preserving and block truncation coding is proposed. An input image is divided into nonoverlapping blocks and each block pixel is assigned one of two representative colors, which are computed with analytic formulas derived from preserving certain moments in the block. A bit map is then generated for each block to represent the pixels' colors. Different uniformity conditions in the representative colors are also identified and utilized to save code bits. Good average compression ratios up to about 13 can be achieved, as shown by experimental results     

Satellite image compression by post-transforms in the wavelet domain

This paper proposes a novel compression scheme with a tunable complexity-rate-distortion trade-off. As images increase in size and resolution, more efficient compression schemes with low complexity are required on-board Earth observation satellites. The standard of the Consultative Committee for Space Data Systems (CCSDS) defines a strip-based compression scheme with the advantages of a low complexity and an easy rate control [CCSDS, Image Data Compression Recommended Standard CCSDS 122.0-B-1 Blue Book, November 2005]. However, future mission specifications expect higher performance in terms of rate-distortion. The scheme proposed in this paper intends to perform better than the CCSDS standard while preserving low complexity and easy rate control. Moreover, to comply with existing on-board devices, the proposed core compression engine still uses the wavelet transform but in association with a linear post-processing inspired from the bandelet transform. The post-transform decomposes a small block of wavelet coefficients on a particular basis. This basis is adaptively selected within a predefined dictionary by rate-distortion optimization. The computational complexity depends upon the dictionary size and of the basis structure. An extremely simple dictionary, reduced to the Hadamard basis, is proposed. The post-transform efficiency is illustrated by experiments on various Earth observation images provided by the French Space Agency (CNES).