Existing 3-D dynamic mesh compression methods directly explore temporal redundancy by predictive coding
and the coded bitstreams are sensitive to transmission errors. In this paper, an efficient and error-resilient
compression paradigm based on Wyner-Ziv coding (WZC) is proposed. We first apply an anisotropic wavelet
transform (AWT) on each frame to explore their spatial redundancy. Then the wavelet coeffcients of every
frame are compressed by a Wyner-Ziv codec which is composed of a nested scalar quantizer and a turbo codes
based Slepian-Wolf codec. Benefiting from the inherent robustness of WZC, the proposed coding scheme can
alleviates the problem of error-propagation associated with conventional predictive coding scheme. Furthermore,
based on wavelet transform, our method can be extended to support progressive coding which is desirable for
the streaming of 3D meshes. Experimental results show that our scheme is competitive with other compression
methods in compression performance. Moreover, our method is more robust when transmission error occurs.
We present an image data-hiding scheme based on near-capacity dirty-paper codes. The scheme achieves high embedding rates by "hiding" information into mid-frequency DCT coefficients among each DCT block of the host image. To reduce the perceptual distortion due to data-hiding, the mid-frequency DCT coefficients are first perceptually scaled according to Watson's model. Then a rate-1/3 projection matrix in conjunction with a rate-1/5 capacity-approaching dirty-paper code is applied. We are able to embed 1500 information bits into 256×256 images, outperforming, under a Gaussian noise attack, currently the best known data-hiding scheme by 33%. Robustness tests against different attacks, such as low-pass filtering, image scaling, and lossy compression, show that our scheme is a good candidate for high-rate image data-hiding applications.
Rate control for video transmission becomes extremely important in "bandwidth-precious" scenarios and added real-time constraints such as joint source channel coding make it even more vital. Hence, there has always been a demand for simple and efficient rate control algorithms. The approximate linear relationship between coding rate (R) and percentage of zeros among the quantized spatial transform coefficients (ρ) is exploited in the present work, to cater to such low-bandwidth, low-delay applications. The current rate control algorithm for H.264 is used as the benchmark for comparison. The extensive experimental results show that ρ-Domain model outperforms the existing algorithm with a more robust rate control, besides yielding a similar or improved Peak Signal to Noise Ratio (PSNR) and being faster
KEYWORDS: Video, Computer programming, Error control coding, Video coding, Video compression, Forward error correction, Video surveillance, Data compression, Signal to noise ratio, Binary data
Based on recent works on Wyner-Ziv coding (or lossy source coding with decoder side information), we consider
the case with noisy channel and addresses distributed joint source-channel coding, while targeting at the important application of scalable video transmission over wireless networks. In Wyner-Ziv coding, after quantization,
Slepian-Wolf coding (SWC) is used to reduce the rate. SWC is traditionally realized by sending syndromes of a
linear channel code. Since syndromes of the channel code can only compress but cannot protect, for transmission
over noisy channels, additional error protection is needed. However, instead of using one channel code for SWC
and one for error protection, our idea is to use a single channel code to achieve both compression and protection.
We replace the traditional syndrome-based SWC scheme by the parity-based one, where only parity bits of the
Slepian-Wolf channel code are sent. If the amount of transmitted parity bits increases above the Slepian-Wolf
limit, the added redundancy is exploited to cope against the noise in the transmission channel. Using IRA codes
for practical parity-based SWC, we design a novel layered Wyner-Ziv video coder which is robust to channel
failures and thus very suitable for wireless communications. Our simulation results show great advantages of the
proposed solution based on joint source-channel coding compared to the traditional approach where source and
channel coding are performed separately.
We examine various issues related to demonstrating real-time channel adaptive video communications for UAVs using the latest-generation H.264 video compression technology. These issues include among others: real-time channel estimation techniques, real-time data rate adaptation techniques in H.264/AVC, latency in encoding, current encoding speeds, transcoding, and scalable video developments in H.264, all as essential steps along the way. These demonstrations will be conducted in a communication laboratory and a limited operational testing environment.
KEYWORDS: Video, Video coding, Computer programming, Data compression, Video compression, Distortion, Quantization, Image quality standards, Scalable video coding, Image processing
Wyner-Ziv coding refers to lossy source coding with side information at the decoder. Recently some practical applications of Wyner-Ziv coding to video compression have been studied due to its advantage of error robustness over standard video coding standards. Based on recent theoretical result on successive Wyner-Ziv coding, we propose in this paper a practical layered Wyner-Ziv video codec using the DCT, nested scalar quantizer (NSQ), and irregular LDPC code based Slepian-Wolf coding (or lossless source coding with side information). The DCT is applied as an approximation to the conditional KLT, which makes the components of the transformed block conditionally independent given the side information. NSQ is a binning scheme that facilitates layered bit-plane coding of the bin indices while reducing the bit rate. LDPC code based Slepian-Wolf coding exploits the correlation between the quantized version of the source and the side information to achieve further compression. Different from previous works, an attractive feature of our proposed system is that video encoding is done only once but decoding allowed at many lower bit rates without quality loss.
This paper proposes an adaptive block-size motion alignment technique in 3D wavelet coding to further exploit temporal correlations across pictures. Similar to B picture in traditional video coding, each macroblock can motion align from forward and/or backward for temporal wavelet de-composition. In each direction, a macroblock may select its partition from one of seven modes - 16x16, 8x16, 16x8, 8x8, 8x4, 4x8 and 4x4 - to allow accurate motion alignment. Furthermore, the rate-distortion optimization criterions are proposed to select motion mode, motion vectors and partition mode. Although the proposed technique greatly improves the accuracy of motion alignment, it does not directly bring the coding efficiency gain because of smaller block size and more block boundaries. Therefore, an overlapped block motion alignment is further proposed to cope with block boundaries and to suppress spatial high-frequency components. The experimental results show the proposed adaptive block-size motion alignment with the overlapped block motion alignment can achieve up to 1.0 dB gain in 3D wavelet video coding. Our 3D wavelet coder outperforms the MC-EZBC for most sequences by 1~2dB and we are doing up to 1.5 dB better than H.264.
Chromosome analysis is an important procedure in clinical and cancer cytogenetics. Efficient image compression techniques are highly desired to accommodate the rapid growth in the use of digital media for archiving, storage and communication of chromosome spread images. In this paper, we propose a new method based on an important characteristic of these images: the regions of interest to cytogeneticists for evaluation and diagnosis are well determined and segmented. Such information is utilized to advantage in our compression algorithm, which combines lossless coding of chromosome regions with lossy-to-lossless coding of the remaining image parts. This is accomplished by first performing a differential operation on chromosome regions for decorrelation, followed by critically sampled integer wavelet transforms on these regions and the remaining image parts. A modified set partitioning in hierarchical trees (SPIHT) algorithm is then used to generate separate embedded bitstreams that allow continuous lossy-to-lossless compression of both chromosome regions and the rest of the image (although lossless coding of the former is commonly used in practice). Experiments on sample chromosome spread images indicate that the proposed approach significantly outperforms several reference compression schemes and the techniques currently employed in commercial systems.
With the recent development of the use of digital media for cytogenetic imaging applications, efficient compression techniques are highly desirable to accommodate the rapid growth of image data. This paper introduces a lossy to lossless coding technique for compression of multiplex fluorescence in situ hybridization (M-FISH) images, based on 3-D set partitioning in hierarchical trees (3-D SPIHT). Using a lifting-based integer wavelet decomposition, the 3-D SPIHT achieves both embedded coding and substantial improvement in lossless compression over the Lempel-Ziv (WinZip) coding which is the current method for archiving M-FISH images. The lossy compression performance of the 3-D SPIHT is also significantly better than that of the 2-D based JPEG-2000.
KEYWORDS: Wavelets, Video, Wavelet transforms, Quantization, 3D video compression, Video compression, Motion estimation, Distortion, 3D video streaming, Data communications
This paper presents a rate-distortion (R-D) optimized 3D wavelet video coder by extending the concept of space- frequency quantization (SFQ) from 2D to 3D. A lifting-based 3D (1T+2D) wavelet transform is deployed to process one part of the sequence at a time continuously, thus eliminating the boundary effects over GOPs. After 3D SFQ, in which an R-D based spatial-temporal tree-pruning process is used in conjunction with uniform quantization of wavelet coefficients, we apply an efficient progressive 3D entropy coder to further compress the quantized coefficients. Even without motion estimation, the new 3D SFQ video coder outperforms MPEG-4 and other 3D wavelet-based video coders for most sequences at the same bit rate.
This paper addresses progressive wavelet image coding within the trellis-coded space-frequency quantization (TCSFQ) framework. A method similar to that in [2] is used to approximately invert TCSFQ when decoding at rates lower than the encoding rate. Our experiments show that the loss incurred for progressive transmission is within one dB in PSNR and that the progressive coding performance of TCSFQ is competitive with that of the celebrated SPIHT coder at all rates.
KEYWORDS: Wavelets, Wavelet transforms, Video, Video coding, 3D image processing, Quantization, 3D imaging standards, Optical filters, Scalable video coding, 3D video streaming
3-D wavelet-based scalable video coding provides a viable alternative to standard MC-DCT coding. However, many current 3-D wavelet coders experience severe boundary effects across group of picture (GOP) boundaries. This paper proposes a memory efficient transform technique via lifting that effectively computes wavelet transforms of a video sequence continuously on the fly, thus eliminating the boundary effects due to limited length of individual GOPs. Coding results show that the proposed scheme completely eliminates the boundary effects and gives superb video playback quality.
This paper describes a prototype telemedicine system for remote 3D radiation treatment planning. Due to voluminous medical image data and image streams generated in interactive frame rate involved in the application, the importance of deploying adjustable lossy to lossless compression techniques is emphasized in order to achieve acceptable performance via various kinds of communication networks. In particular, the compression of the data substantially reduces the transmission time and therefore allows large-scale radiation distribution simulation and interactive volume visualization using remote supercomputing resources in a timely fashion. The compression algorithms currently used in the software we developed are JPEG and H.263 lossy methods and Lempel-Ziv (LZ77) lossless methods. Both objective and subjective assessment of the effect of lossy compression methods on the volume data are conducted. Favorable results are obtained showing that substantial compression ratio is achievable within distortion tolerance. From our experience, we conclude that 30dB (PSNR) is about the lower bound to achieve acceptable quality when applying lossy compression to anatomy volume data (e.g. CT). For computer simulated data, much higher PSNR (up to 100dB) is expectable. This work not only introduces such novel approach for delivering medical services that will have significant impact on the existing cooperative image-based services, but also provides a platform for the physicians to assess the effects of lossy compression techniques on the diagnostic and aesthetic appearance of medical imaging.
KEYWORDS: Wavelets, 3D modeling, Image compression, 3D image processing, Wavelet transforms, 3D acquisition, Performance modeling, Data modeling, Data centers, Medical imaging
We examine progressive lossy to lossless compression of medical volumetric data using three-dimensional (3D) integer wavelet packet transforms and set partitioning in hierarchical trees (SPIHT). To achieve good lossy coding performance, we describe a 3D integer wavelet packet transform that allows implicit bit shifting of wavelet coefficients to approximate a 3D unitary transformation. We also address context modeling for efficient entropy coding within the SPIHT framework. Both lossy and lossless coding performances are better than those reported recently in reference one.
Recent progresses in wavelet image coding have brought the field into its maturity. Major developments in the process are rate-distortion (R-D) based wavelet packet transformation, zerotree quantization, subband classification and trellis- coded quantization, and sophisticated context modeling in entropy coding. Drawing from past experience and recent insight, we propose a new wavelet image coding technique with trellis coded space-frequency quantization (TCSFQ). TCSFQ aims to explore space-frequency characterizations of wavelet image representations via R-D optimized zerotree pruning, trellis coded quantization, and context modeling in entropy coding. Experiments indicate that the TCSFQ coder achieves twice as much compression as the baseline JPEG coder does at the same peak signal to noise ratio (PSNR), making it better than all other coders described in the literature.
We examine color quantization of images using trellis coded quantization (TCQ). Together with a simple halftoning scheme, an eight-bit trellis coded color quantizer reproduces images that are visually indistinguishable from the 24-bit originals. The proposed algorithm can be viewed as a predictive trellis coded color quantization scheme. It is universal in the sense that no training or look-up table is needed. The complexity of TCQ is linear with respect to image size, making trellis coded color quantization suitable for interactive graphics and a window-based display environment.
We extend the work of Sherwood and Zeger to progressive video coding for noisy channels. By utilizing a 3D extension of the set partitioning in hierarchical trees (SPIHT) algorithm, we cascade the resulting 3D SPIHT video coder with a rate-compatible punctured convolutional channel coder for transmission of video over a binary symmetric channel. Progressive coding is achieved by increasing the target rate of the 3D embedded SPIHT video coder as the channel condition improves. The performance of our proposed coding system is acceptable at low transmission rate and bad channel conditions. Its low complexity makes it suitable for emerging applications such as video over wireless channels.
We address multiresolutional encoding and decoding within the embedded zerotree wavelet (EZW) framework for both images and video. By varying a resolution parameter, one can obtain decoded images at different resolutions from one single encoded bitstream, which is already rate scalable for EZW coders. Similarly one can decode video sequences at different rates and different spatial and temporal resolutions from one bitstream. Furthermore, a layered bitstream can be generated with multiresolutional encoding, from which the higher resolution layers can be used to increase the spatial/temporal resolution of the images/video obtained from the low resolution layer. In other words, we have achieved full scalability in rate and partial scalability in space and time. This added spatial/temporal scalability is significant for emerging multimedia applications such as fast decoding, image/video database browsing, telemedicine, multipoint video conferencing, and distance learning.
We study the performance difference of the discrete cosine transform (DCT) and the wavelet transform for both image and video coding, while comparing the other aspects of the coding system on an equal footing. Based on the state-of-the-art coding techniques, we point out that, for still images, the performance gap between DCT and wavelet based coding is within one dB in PSNR at the same bitrate. This is in contrast to the common perception that the wavelet transform is much superior to the DCT for image compression. For video coding, the advantage of using the wavelet transform over the DCT is even less pronounced.
This paper introduces a new approach to inverse halftoning using nonorthogonal wavelets. The distinct features of this wavelet-based approach are: a) edge information in the highpass wavelet images of a halftone is extracted and used to assist inverse halftoning, b) cross-scale correlations in the multiscale wavelet decomposition are used for removing background halftoning noise while preserving important edges in the wavelet lowpass image, c) experiments show that our simple wavelet-based approach outperforms the best results obtained from inverse halftoning methods published in the literature, which are iterative in nature.
This paper introduces a new approach to deblocking of JPEG compressed images using over-complete wavelet representations. By exploiting cross-scale correlations among wavelet coefficients, edge information in the JPEG compressed images is extracted and protected, while blocky noise in the smooth background regions is smoothed out in the wavelet domain. Compared with the iterative methods reported in the literature, our simple wavelet-based method has much lower computational complexity, yet it is capable of achieving the same PSNR improvement as the best iterative method, and giving visually very pleasing images as well.
We address efficient context modeling in arithmetic coding for wavelet image compression. Quantized
highpass wavelet coefficients are first mapped into a binary source, followed by high order context
modeling in arithmetic coding. A blending technique is used to combine results of context modeling
of different orders into a single probability estimate. Experiments show that an arithmetic coder with
efficient context modeling is capable of achieving a 10% bitrate saving (or 0.5 dB gain in PSNR) over
a zeroth order adaptive arithmetic coder in high performance wavelet image coders.
We address efficient context modeling in arithmetic coding for wavelet image compression. Quantized highpass wavelet coefficients are first mapped into a binary source, followed by high order context modeling in arithmetic coding. A blending technique is used to combine results of context modeling of different orders into a single probability estimate. Experiments show that an arithmetic coder with efficient context modeling is capable of achieving a 10 percent bitrate saving over a zeroth order adaptive arithmetic coder in high performance wavelet image coders.
A novel wavelet packet image coder is introduced in this paper. It is based on our previous work on wavelet image coding using space-frequency quantization (SFQ), in which zerotree quantization and scalar quantization are jointly optimized in a rate-distortion sense. In this paper, we extend the powerful SFQ coding paradigm from the wavelet transform to the more general wavelet packet transformation. The resulting wavelet packet coder offers a universal transform coding framework within the constraints of filter bank structures by allowing joint transform and quantizer design without assuming a priori statistics of the input image. In other words, the new coder adaptively chooses the representation to suit the image and the quantization to suit the representation. Experimental results show that, for some image classes, our new coder is capable of achieving the best coding performances among those in the published literature.
Recently, a new class of image coding algorithms coupling standard scalar quantization of frequency coefficients with tree-structured quantization has attracted wide attention because its good performance appears to confirm the promised efficiencies of hierarchical representation. This paper addresses the problem of how spatial quantization modes and standard scalar quantization can be applied in a jointly optimal fashion in an image coder. We consider zerotree quantization and the simplest form of scalar quantizations, and we formalize the problem of optimizing their joint application and we develop an image coding algorithm for solving the resulting optimization problem. Despite the basic form of the two quantizers considered, the resulting algorithm demonstrates coding performance that is competitive the very best coding algorithms in the literature.
We examine the question of how to choose a time-varying filter bank representation for a signal which is optimal with respect to an additive cost function. The tree structure gives segmentations of the signal in frequency, while the time-varying nature of the tree gives segmentations in time. We present an efficient algorithm which finds the optimal basis, given the constraint that the time and frequency segmentations are binary. Extension to multiple dimensions is simple. We verify that the algorithm indeed produces a lower cost representation than any of the wavelet packet representations for compression of images using a simple rate-distortion cost.
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