Английские материалы
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| Авторы |
Название статьи |
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| Byeungwoo Jeon, and Jechang Jeong |
Blocking Artifacts Reduction in Image Compression with Block Boundary Discontinuity Criterion |
Abstract—This paper proposes a novel blocking artifacts reduction
method based on the notion that the blocking artifacts
are caused by heavy accuracy loss of transform coefficients in the
quantization process. We define the block boundary discontinuity
measure as the sum of the squared differences of pixel values
along the block boundary. The proposed method compensates
for selected transform coefficients so that the resultant image
has a minimum block boundary discontinuity. The proposed
method does not require a particular transform domain where
the compensation should take place; therefore, an appropriate
transform domain can be selected at the user’s discretion. In
the experiments, the scheme is applied to DCT-based compressed
images to show its performance.
RAR 427 кбайт
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| Hsien-Chung Wei, Pao-Chin Tsai, and Jia-Shung Wang |
Three-Sided Side Match Finite-State Vector Quantization |
Abstract—Several low bit-rate still-image compression methods
have been presented for the past two years, such as SPHIT, hybrid
VQ, and the Wu–Chen method. In particular, the image “Lena”
can be compressed using less than 0.15 bpp at 31.4 dB or higher.
These methods exercise the analysis techniques (wavelet or subband)
before distributing the bit rate to each piece of an image,
thus the dilemma between bit rate and distortion can be solved. In
this paper,we propose a simple but comparable method that adopts
the technique of side match VQ only. The side match vector quantization
(SMVQ) is an effective VQ coding scheme at low bit-rate.
The conventional side match (two-sided) VQ utilizes the codeword
information of two neighboring blocks to predict the state codebook
of an input vector. In this paper, we propose a hierarchical
three-sided side match finite-state vector quantization (HTSMVQ)
method that can: 1) make the state codebook size as small as possible
—the size is reduced to one if the prediction can perform perfectly;
2) improve the prediction quality for edge blocks; and 3)
regularly refresh the codewords to alleviate the error propagation
of side match. In the simulation results, the image “Lena” can be
coded with PSNR 34.682 dB at 0.25 bpp. It is better than SPIHT,
EZW, FSSQ and hybrid VQ with 34.1, 33.17, 33.1, and 33.7 dB,
respectively. At a bit rate lower than 0.15 bpp, only the enhanced
version of EZW performs better than our method, about 0.14 dB.
Thus, each input vector is encoded using its own state codebook.
RAR 189 кбайт
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| Ralph Neff and Avideh Zakhor |
Modulus Quantization for Matching-Pursuit Video Coding |
Abstract—Overcomplete signal decomposition using matching
pursuits has been shown to be an efficient technique for coding
motion-residual images in a hybrid video coder. Unlike orthogonal
decomposition, matching pursuit uses an in-the-loop modulus
quantizer which must be specified before coding begins. This
complicates the quantizer design, since the optimal quantizer depends
on the statistics of the matching-pursuit coefficients which in
turn depend on the in-loop quantizer actually used. In this paper,
we address the modulus quantizer design issue, specifically developing
frame-adaptive quantization schemes for the matching-pursuit
video coder. Adaptive dead-zone subtraction is shown to reduce
the information content of the modulus source, and a uniform
threshhold quantizer is shown to be optimal for the resulting
source. Practical two-pass and one-pass algorithms are developed
to jointly determine the quantizer parameters and the number of
coded basis functions in order to minimize coding distortion for a
given rate. The compromise one-pass scheme performs nearly as
well as the full two-pass algorithm, but with the same complexity
as a fixed-quantizer design. The adaptive schemes are shown to
outperform the fixed quantizer used in earlier works, especially at
high bit rates, where the gain is as high as 1.7 dB.
RAR 2015 кбайт
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| Jin Soo Choi, Yong Han Kim, Ho-Jang Lee, In-Sung Park, Myoung Ho Lee, and Chieteuk Ahn |
Geometry Compression of 3-D Mesh Models Using Predictive Two-Stage Quantization |
Chieteuk Ahn
Abstract—In conventional predictive quantization schemes
for 3-D mesh geometry, excessively large residuals or prediction
errors, although occasional, lead to visually unacceptable geometric
distortion. This is due to the fact that they cannot limit the
maximum quantization error within a given bound. In order to
completely eliminate the visually unacceptable distortion caused
by large residuals, we propose a predictive two-stage quantization
scheme. This scheme is very similar to the conventional DPCM,
except that the embedded quantizer is replaced by a series of two
quantizers. Each quantizer output is further compressed by an
arithmetic code. When applied to typical 3-D mesh models, the
scheme performs much better than the conventional predictive
quantization methods and, depending upon input models, even
than the MPEG-4 compression method for 3-D mesh geometry
both in rate-distortion sense and in subjective viewing.
RAR 418 кбайт
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| Guobin Shen and Ming L. Liou |
An Efficient Codebook Post-Processing Technique and a Window-Based Fast-Search Algorithm for Image Vector Quantization |
Abstract—Vector quantization is an efficient image-coding technique
to achieve a very low bit-rate compression. Furthermore, a
lower bit rate can be achieved by equipping the vector quantizer
with a memory unit or feedback loop so as to utilize the intervector
correlation. For example, predictive vector quantization exploits
the linear inter-vector correlation in the spatial domain by
a linear vector prediction. Despite the better performance of this
kind of vector quantizer, they are usually much more complex. In
this paper, we proposed a simple but efficient codebook post-processing
technique which enables the vector quantizer to possess
higher correlation preservation property. As will be shown, the
proposed post-processing technique leads to much higher interindex
correlation, or equivalently, smaller first-order (or higher
order) entropy.
Based on the special pattern of the codebook imposed by the
post-processing technique, a window-based fast search (WBFS) algorithm
is proposed. The WBFS algorithm not only accelerates the
vector quantization processing, but also results in better rate-distortion
performance.
RAR 406 кбайт
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| James E. Fowler |
Adaptive Vector Quantization for Efficient Zerotree-Based Coding of Video with Nonstationary Statistics |
Abstract—A new system for intraframe coding of video is described.
This system combines zerotrees of vectors of wavelet coefficients
and the generalized-threshold-replenishment (GTR) technique
for adaptive vector quantization (AVQ).Adata structure, the
vector zerotree (VZT), is introduced to identify trees of insignificant
vectors, i.e., those vectors of wavelet coefficients in a dyadic
subband decomposition that are to be coded as zero. GTR coders
are then applied to each subband to efficiently code the significant
vectors by way of adapting to their changing statistics. Both VZT
generation and GTR coding are based upon minimization of criteria
involving both rate and distortion. In addition, perceptual
performance is improved by invoking simple, perceptually motivated
weighting in both the VZT and the GTR coders. Our experimental
findings indicate that the described VZTGTR system handles
dramatic changes in image statistics, such as those due to a
scene change, more efficiently than wavelet-based techniques employing
nonadaptive scalar quantizers.
RAR 398 кбайт
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| Seung-Kwon Paek and Lee-Sup Kim |
A Real-Time Wavelet Vector Quantization Algorithm and Its VLSI Architecture |
Abstract—In this paper, a real-time wavelet image compression
algorithm using vector quantization and its VLSI architecture
are proposed. The proposed zerotree wavelet vector quantization
(WVQ) algorithm focuses on the problem of how to reduce the
computation time to encode wavelet images with high coding
efficiency. A conventional wavelet image-compression algorithm
exploits the tree structure of wavelet coefficients coupled with
scalar quantization. However, they can not provide the real-time
computation because they use iterative methods to decide zerotrees.
In contrast, the zerotree WVQ algorithm predicts in
real-time zero-vector trees of insignificant wavelet vectors by a
noniterative decision rule and then encodes significant wavelet
vectors by the classified VQ. These cause the zerotree WVQ
algorithm to provide the best compromise between the coding
performance and the computation time. The noniterative decision
rule was extracted by the simulation results, which are based on
the statistical characteristics of wavelet images. Moreover, the
zerotree WVQ exploits the multistage VQ to encode the lowest
frequency subband, which is generally known to be robust to wireless
channel errors. The proposed WVQ VLSI architecture has
only one VQ module to execute in real-time the proposed zerotree
WVQ algorithm by utilizing the vacant cycles for zero-vector trees
which are not transmitted. And the VQ module has only + 1
processing elements (PE's) for the real-time minimum distance
calculation, where the codebook size is . PE's are for Euclidean
distance calculation and a PE is for parallel distance comparison.
Compared with conventional architectures, the proposed VLSI
architectures has very cost-effective hardware (H/W) to calculate
zerotree WVQ algorithm in real time. Therefore, the zerotree
WVQ algorithm and its VLSI architectures are very suitable to
wireless image communication, because they provide high coding
efficiency, real-time computation, and cost-effective H/W.
image-compression techniques robust to transmission channel
errors are essential to wireless image communication, because
wireless communication channels suffer from burst errors in
which a large number of consecutive bits are lost or corrupted
by the channel-fading effect. The conventional image-coding
standards are very susceptible to transmission errors, and hence,
they need powerful error-correction codes. Therefore, it is desirable
to design a robust image-coding technique, which has a
high compression ratio and produces acceptable image quality
over a fading channel. Finally, we should consider image compression
algorithms and their VLSI architectures which allow
portable decoders with small size, low-power consumption, and
acceptable reconstructed image quality.
RAR 694 кбайт
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| Hugh. Q. Cao and Weiping Li |
A Fast Search Algorithm for Vector Quantization Using a Directed Graph |
Abstract—A fast search algorithm for vector quantization (VQ)
is presented in this letter. This approach provides a practical solution
to the implementation of a multilevel search based on a specially
designed directed graph (DG). An algorithm is also given to
find the optimal DG for any given practical source. Simulation results
applying this approach to still images have shown that it can
reduce searching complexity to 3% of the exhaustive search vector
quantization (ESVQ) while introducing only negligible searching
errors. It has also been shown that the searching complexity is
close to a linear growth with the bit rate rather than an exponential
growth in ESVQ.
RAR 426 кбайт
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| Woontack Woo and Antonio Ortega |
Optimal Blockwise Dependent Quantization for Stereo Image Coding |
Abstract—Research in coding of stereo images has focused
mostly on the issue of disparity estimation to exploit the redundancy
between the two images in a stereo pair, with less attention
being devoted to the equally important problem of allocating
bits between the two images. This bit-allocation problem is
complicated by the dependencies arising from using a prediction
based on the quantized reference images. In this paper, we
address the problem of blockwise bit allocation for coding of
stereo images and show how, given the special characteristics
of the disparity field, one can achieve an optimal solution with
reasonable complexity, whereas in similar problems in motioncompensated
video only approximate solutions are feasible. We
present algorithms based on dynamic programming that provide
the optimal blockwise bit allocation. Our experiments based
on a modified JPEG coder show that the proposed scheme
achieves higher mean peak signal-to-noise ratio over the two
frames (0.2–0.5 dB improvements) as compared with blockwise
independent quantization. We also propose a fast algorithm that
provides most of the gain at a fraction of the complexity.
RAR 246 кбайт
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| Nam Ik Cho, Heesub Lee, and Sang Uk Lee |
An Adaptive Quantization Algorithm for Video Coding |
Abstract—This paper proposes an adaptive quantization algorithm
for video coding using the information obtained from the
previously encoded image. Before quantizing the discrete cosine
transform coefficients, the properties of reconstruction error of
each macro block (MB) are estimated from the previous frame.
For the estimation of the error of current MB, a block with the
size of MB in the previous frame is chosen. Since the original and
reconstructed images of the previous frame are available in the
encoder, we can evaluate the tendency of reconstruction error
of this block in advance. Then, this error is considered as the
expected error of the current MB if it is quantized with the same
step size and bit rate. Comparing the error of the MB with the
average of overall MB’s, if it is larger than the average, a small
step size is given for this MB, and vice versa. As a result, the
error distribution of the MB is more concentrated to the average,
yielding low variance and improved image quality. Especially for
low bit application, the proposed algorithm yields much smaller
error variance and higher peak signal-to-noise ratio compared to
the conventional TM5. We also propose a modified algorithm for
efficient hardware implementation.
RAR 328 кбайт
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| Hyun Wook Park and Yung Lyul Lee |
A Postprocessing Method for Reducing Quantization Effects in Low Bit-Rate Moving Picture Coding |
Abstract—The reconstructed images from highly compressed
MPEG data have noticeable image degradations, such as blocking
artifacts near the block boundaries, corner outliers at crosspoints
of blocks, and ringing noise near image edges because the MPEG
quantizes the transformed coefficients of 8 . 8 pixel blocks.
A postprocessing algorithm is proposed to reduce quantization
effects, such as blocking artifacts, corner outliers, and ringing
noise, in MPEG-decompressed images. The proposed postprocessing
algorithm reduces the quantization effects adaptively by
using both spatial frequency and temporal information extracted
from the compressed data. The blocking artifacts are reduced by
one-dimensional (1-D) horizontal and vertical low-pass filtering
(LPF), and the ringing noise is reduced by two-dimensional (2-D)
signal-adaptive filtering (SAF). A comparison study of the peak
signal-to-noise ratio (PSNR) and the computation complexity
analysis between the proposed algorithm and the MPEG-4 VM
(verification model) postprocessing algorithm is performed by
computer simulation with several image sequences. According
to the comparison study of PSNR and computation complexity
analysis, the proposed algorithm shows better performance than
the VM postprocessing algorithm, whereas the subjective image
qualities of both algorithms are similar.
RAR 539 кбайт
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| Chun Wang, Hugh Q. Cao, Weiping Li, and Kenneth K. Tzeng, Fellow |
Lattice Labeling Algorithms for Vector Quantization |
Abstract— Labeling algorithms for Construction-A and
Construction-B lattices with respect to pyramid boundaries
are presented. The algorithms are developed based on
relations between lattices and linear block codes as well as on
transformations among several specifically defined lattices and
their translations. The mechanism for the construction of these
algorithms can be considered as an extension of that given by
Fischer. The algorithms are noted to achieve 100% efficiency
in utilizing index bits for binary representations. Furthermore,
it is determined that many important lattices (E8, .16; . . .) can
be indexed to arbitrary norms and dimensions. The complexity
of these algorithms in terms of both memory and computation
is quite low and thus it is possible to develop practical lattice
vector quantizers of large norms and high dimensions using
these algorithms.
RAR 781 кбайт
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| R. Chandramouli, N. Ranganathan, and Shivaraman J. Ramadoss |
Adaptive Quantization and Fast Error-Resilient Entropy Coding for Image Transmission |
Abstract—Recently, there has been an outburst of research
in image and video compression for transmission over noisy
channels. Channel matched source quantizer design has gained
prominence. Further, the presence of variable-length codes in
compression standards like the JPEG and the MPEG has made
the problem more interesting. Error resilient entropy coding
(EREC) has emerged as a new and effective method to combat
catastrophic loss in the received signal due to burst and random
errors. In this paper, we propose a new channel-matched adaptive
quantizer for JPEG image compression. A slow, frequencynonselective
Rayleigh fading channel model is assumed. The
optimal quantizer that matches the human visibility threshold
and the channel bit-error rate is derived. Further, a new fast
error-resilient entropy code (FEREC) that exploits the statistics
of the JPEG compressed data is proposed. The proposed FEREC
algorithm is shown to be almost twice as fast as EREC in encoding
the data, and hence the error resilience capability is also observed
to be significantly better. On an average, a 5% decrease in
the number of significantly corrupted received image blocks is
observed with FEREC. Upto a 2-dB improvement in the peak
signal-to-noise ratio of the received image is also achieved.
RAR 315 кбайт
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| Syed A. Rizvi and Nasser M. Nasrabadi |
Finite-State Residual Vector Quantization Using a Tree-Structured Competitive Neural Network |
Abstract—Finite-state vector quantization (FSVQ) is known to
give better performance than the memoryless vector quantization
(VQ). This paper presents a new FSVQ scheme, called finite-state
residual vector quantization (FSRVQ), in which each state uses
a residual vector quantizer (RVQ) to encode the input vector.
This scheme differs from the conventional FSVQ in that the state-
RVQ codebooks encode the residual vectors instead of the original
vectors. A neural network predictor estimates the current block
based on the four previously encoded blocks. The predicted vector
is then used to identify the current state as well as to generate
a residual vector (the difference between the current vector
and the predicted vector). This residual vector is encoded using
the current state-RVQ codebooks. A major task in designing
our proposed FSRVQ is the joint optimization of the next-state
codebook and the state-RVQ codebooks. This is achieved by
introducing a novel tree-structured competitive neural network
in which the first layer implements the next-state function, and
each branch of the tree implements the corresponding state-
RVQ. A joint training algorithm is also developed that mutually
optimizes the next-state and the state-RVQ codebooks for the
proposed FSRVQ. Joint optimization of the next-state function
and the state-RVQ codebooks eliminates a large number of
redundant states in the conventional FSVQ design; consequently,
the memory requirements are substantially reduced in the proposed
FSRVQ scheme. The proposed FSRVQ can be designed
for high bit rates due to its very low memory requirements and
the low search complexity of the state-RVQ’s. Simulation results
show that the proposed FSRVQ scheme outperforms conventional
FSVQ schemes both in terms of memory requirements and the
visual quality of the reconstructed image. The proposed FSRVQ
scheme also outperforms JPEG (the current standard for still
image compression) at low bit rates.
RAR 1822 кбайт
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| Tung-Shou Chen and Chin-Chen Chang |
Diagonal Axes Method (DAM): A Fast Search Algorithm for Vector Quantization |
Abstract—Vector quantization (VQ) is a fundamental technique for
image compression. But it takes time to search for a similar codeword in
a codebook. Thus, the codebook search is one of the major bottlenecks in
VQ. In this paper, we propose a new search algorithm which is used to
speed up both the codebook generation and the encoding. We call it the
diagonal axes method (DAM). This new algorithm contains two major
techniques: diagonal axes analysis (DAA) and orthogonal checking (OC).
Since most of these procedures simply involve additions and subtractions,
DAM is more efficient than some other related algorithms. Simulation
results confirm this effectiveness.
RAR 223 кбайт
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| Jiebo Luo, Chang Wen Chen, Kevin J. Parker and Thomas S. Huang |
A Scene Adaptive and Signal Adaptive Quantization for Subband Image and Video Compression Using Wavelets |
Abstract—Discrete wavelet transform (DWT) provides an advantageous
framework of multiresolution space-frequency representation
with promising applications in image processing. The
challenge as well as the opportunity in wavelet-based compression
is to exploit the characteristics of the subband coefficients with
respect to both spectral and spatial localities. A common problem
with many existing quantization methods is that the inherent
image structures are severely distorted with coarse quantization.
Observation shows that subband coefficients with the same magnitude
generally do not have the same perceptual importance;
this depends on whether or not they belong to clustered scene
structures. We propose in this paper a novel scene adaptive and
signal adaptive quantization scheme capable of exploiting both
the spectral and spatial localization properties resulting from
wavelet transform. The proposed quantization is implemented as
a maximum a posteriori probability (MAP) estimation-based clustering
process in which subband coefficients are quantized to their
cluster means, subject to local spatial constraints. The intensity
distribution of each cluster within a subband is modeled by an
optimal Laplacian source to achieve the signal adaptivity, while
spatial constraints are enforced by appropriate Gibbs random
fields (GRF) to achieve the scene adaptivity. Consequently, with
spatially isolated coefficients removed and clustered coefficients
retained at the same time, the available bits are allocated to
visually important scene structures so that the information loss
is least perceptible. Furthermore, the reconstruction noise in the
decompressed image can be suppressed using another GRF-based
enhancement algorithm. Experimental results have shown the
potentials of this quantization scheme for low bit-rate image and
video compression.
RAR 998 кбайт
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