| Seongman Kim, Seunghyeon Rhee, Jun Geun Jeon, and Kyu Tae Park |
Interframe Coding Using Two-Stage Variable Block-Size Multiresolution Motion Estimation and Wavelet Decomposition |
Abstract— In this paper, we propose a two-stage variable
block-size multiresolution motion estimation (MRME) algorithm.
In this algorithm, a method to reduce the amount of motion
information is developed, and a bit allocation method minimizing
the sum of the motion information and the prediction error is
obtained in the wavelet transform domain.
In the first stage of the proposed scheme, we utilize a set of
wavelet components of the four subbands in the lowest resolution.
These motion vectors are directly used as motion vectors for the
lowest subband, and are scaled into the initial biases for other
subbands at every layer of the wavelet pyramid. In the second
stage, the bottom-up construction of a quadtree based on the
merge operation is performed. The proposed scheme reduces the
uncompressed bit rate of 8 bits/pixel into 0.212 bits/pixel at 41.1
dB of PSNR for the “Claire” sequence, which can be regarded as
nearly an 11% decrease compared with the conventional method.
RAR 378 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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| 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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| Shipeng Li, and Weiping Li, Fellow |
Shape-Adaptive Discrete Wavelet Transforms for Arbitrarily Shaped Visual Object Coding |
Abstract—This paper presents a shape-adaptive wavelet
coding technique for coding arbitrarily shaped still texture. This
technique includes shape-adaptive discrete wavelet transforms
(SA-DWT’s) and extentions of zerotree entropy (ZTE) coding
and embedded zerotree wavelet (EZW) coding. Shape-adaptive
wavelet coding is needed for efficiently coding arbitrarily shaped
visual objects, which is essential for object-oriented multimedia
applications. The challenge is to achieve high coding efficiency
while satisfying the functionality of representing arbitrarily
shaped visual texture. One of the features of the SA-DWT’s is
that the number of coefficients after SA-DWT’s is identical to the
number of pixels in the original arbitrarily shaped visual object.
Another feature of the SA-DWT is that the spatial correlation,
locality properties of wavelet transforms, and self-similarity across
subbands are well preserved in the SA-DWT. Also, for a rectangular
region, the SA-DWT becomes identical to the conventional
wavelet transforms. For the same reason, the extentions of ZTE
and EZW to coding arbitrarily shaped visual objects carefully
treat “don’t care” nodes in the wavelet trees. Comparison of
shape-adaptive wavelet coding with other coding schemes for
arbitrarily shaped visual objects shows that shape-adaptive
wavelet coding always achieves better coding efficiency than
other schemes. One implementation of the shape-adaptive wavelet
coding technique has been included in the new multimedia coding
standard MPEG-4 for coding arbitrarily shaped still texture.
Software implementation is also available.
RAR 2840 кбайт
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| David B. H. Tay |
Rationalizing the Coefficients of Popular Biorthogonal Wavelet Filters |
Abstract—Many wavelet filters found in the literature have irrational
coefficients and thus require infinite precision implementation.
One of the most popular filter pairs is the “9/7” biorthogonal
pair of Cohen, Daubechies, and Feauveau, which is adopted
in the FBI finger-print compression standard. We present here a
technique to rationalize the coeffcients of wavelet filters that will
preserve biorthogonality and perfect reconstruction. Furthermore,
most of the zeros at = 1 will also be preserved. These zeros are
important for achieving regularity. The rationalized coefficients filters
have characteristics that are close to the original irrational coefficients
filters. Three popular pairs of filters, which include the
“9/7” pair, will be considered.
RAR 710 кбайт
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| Ashraf A. Kassim and Lifeng Zhao |
Rate-Scalable Object-Based Wavelet Codec with Implicit Shape Coding |
Abstract—In this paper, we present an embedded approach for
coding image regions with arbitrary shapes. Our scheme takes
a different approach by separating the objects in the transform
domain instead of the image domain so that only one transform
for the entire image is required. We define a new shape-adaptive
embedded zerotree wavelet coding (SA-EZW) technique for
encoding the coefficients corresponding to specific objects in
gray-scale and color-image segments by implicitly representing
their shapes, thereby forgoing the need for separately coding the
region boundary. At our decoder, the shape information can be
recovered without separate and explicit shape coding. The implicit
shape coding enables the bit stream for the object to be fully rate
scalable, since no explicit bit allocation is needed for the object
shape. This makes it particularly suitable when content-based
functionalities are desired in situations where the user bit rate is
constrained and enables precise bit-rate control while avoiding
the problem of contour coding. We show that our algorithm
sufficiently addresses the issue of content-based scalability and
improved coding efficiency when compared with the “chroma
keying” technique, an implicit shape-coding technique which is
adopted by the current MPEG-4 standard.
RAR 820 кбайт
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| Chengjiang Lin, Bo Zhang, and Yuan F. Zheng |
Packed Integer Wavelet Transform Constructed by Lifting Scheme |
Abstract—A new method for speeding up the integer wavelet
transforms constructed by the lifting scheme is proposed. The proposed
method packs multiple pixels (wavelet coefficients) in a single
word; therefore, it can make use of the 32-bit or 64-bit computational
capability of modern computers to accomplish multiple addition/
subtraction operations in one instruction cycle. As a result,
our method can save the decomposition/reconstruction time by up
to 37% on 32-bit machines and require much less working memory
in comparison with the original wavelet transform algorithms.
RAR 106 кбайт
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| CKai Bao and Xiang-Gen Xia |
Image Compression Using a New Discrete Multiwavelet Transform and a New Embedded Vector Quantization |
Abstract—An embedded image compression scheme using discrete
multiwavelet transform (DMWT) is proposed in this paper.
The proposed coding scheme is based on a new prefilter design for
DMWT and a new embedded coding algorithm which combines
scalar quantization and 2 2 vector quantization (VQ). A new algorithm
for embeddedVQcodebook generation is proposed, which
is shown to have a better performance than the current schemes.
The performance of the proposed compression scheme is comparable
to the one of the SPIHT algorithm.
RAR 588 кбайт
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| Hyuk Choi and Taejeong Kim |
Blocking-Artifact Reduction in Block-Coded Images Using Wavelet-Based Subband Decomposition |
Abstract—We propose a post-processing method in the wavelet
transform domain that can significantly reduce the blocking effects
in low-bit-rate block-transform-coded images. Although the
quantization noise of transform coefficients is the sole source of
error in a coded image, the properties of block transform make
the errors appear in two categories: blocky noise, which causes
blocking effects, and granular (nonblocky) noise. Noting that subband
coding does not suffer from blocky noise, the proposed technique
is designed to work in the subband domain. Once a coded
image is decomposed into subbands by wavelet filters, most energy
of the blocky noise exists on the predetermined block boundaries of
their corresponding subbands. We can reduce the blocky noise by
a linear minimum mean square error filter, which fully exploits the
characteristics of the signal and noise components in each subband.
After the blocky noise is reduced, the granular noise can further be
decreased by exploiting its nonstructuredness. Computer simulations
show that the proposed method visibly reduces the blocking
effects in reconstructed images and yields better PSNR improvement.
In this paper, we divide the blocking artifacts into two categories.
RAR 136 кбайт
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| Detlev Marpe, Gabi Blдttermann, Jens Ricke, and Peter MaaЯ |
A Two-Layered Wavelet-Based Algorithm for Efficient Lossless and Lossy Image Compression |
Abstract—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 lossless image-coding schemes. Compared to other combined
lossy/lossless coding 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.
RAR 234 кбайт
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| Seung-Kwon Paek and Lee-Sup Kim |
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.
RAR 694 кбайт
|
?
|
| Ke Shen, and Edward J. Delp, Fellow |
Wavelet Based Rate Scalable Video Compression |
Abstract—In this paper, we present a new wavelet based rate
scalable video compression algorithm. We will refer to this new
technique as the Scalable Adaptive Motion Compensated Wavelet
(SAMCoW) algorithm. SAMCoW uses motion compensation
to reduce temporal redundancy. The prediction error frames and
the intracoded frames are encoded using an approach similar
to the embedded zerotree wavelet (EZW) coder. An adaptive
motion compensation (AMC) scheme is described to address
error propagation problems. We show that, using our AMC
scheme, the quality of the decoded video can be maintained
at various data rates. We also describe an EZW approach
that exploits the interdependency between color components
in the luminance/chrominance color space. We show that, in
addition to providing a wide range of rate scalability, our encoder
achieves comparable performance to the more traditional hybrid
video coders, such as MPEG1 and H.263. Furthermore, our
coding scheme allows the data rate to be dynamically changed
during decoding, which is very appealing for network-oriented
applications.
RAR 1018 кбайт
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| Iraj Sodagar, Hung-Ju Lee, Paul Hatrack, and Ya-Qin Zhang, Fellow |
Scalable Wavelet Coding for Synthetic/Natural Hybrid Images |
Abstract— This paper describes the texture representation
scheme adopted for MPEG-4 synthetic/natural hybrid coding
(SNHC) of texture maps and images. The scheme is based on
the concept of multiscale zerotree wavelet entropy (MZTE)
coding technique, which provides many levels of scalability
layers in terms of either spatial resolutions or picture quality.
MZTE, with three different modes (single-Q, multi-Q, and
bilevel), provides much improved compression efficiency and
fine-gradual scalabilities, which are ideal for hybrid coding of
texture maps and natural images. The MZTE scheme is adopted
as the baseline technique for the visual texture coding profile in
both the MPEG-4 video group and SNHC group. The test results
are presented in comparison with those coded by the baseline
JPEG scheme for different types of input images. MZTE was
also rated as one of the top five schemes in terms of compression
efficiency in the JPEG2000 November 1997 evaluation, among
27 submitted proposals.
RAR 949 кбайт
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| Hong Man, Faouzi Kossentini, and Mark J. T. Smith, Fellow |
A Family of Efficient and Channel Error Resilient Wavelet/Subband Image Coders |
Abstract—We present a new wavelet/subband framework that
allows the efficient and effective quantization/coding of subband
coefficients in both noiseless and noisy channel environments. Two
different models, one based on a zero-tree structure and another
based on a quadtree and context-based modeling structure, are
introduced for coding the locations of significant subband coeffi-
cients. Then, several multistage residual lattice vector quantizers
are proposed for the quantization of such coefficients. The proposed
framework features relatively simple modeling and quantization/
coding structures that produce a bit stream containing
two distinct bit sequences, which can then be protected differently
according to their importance and channel noise sensitivity
levels. The resulting wavelet/subband image coding algorithms
provide good tradeoffs between compression performance and
resilience to channel errors. In fact, experimental results indicate
that for both noiseless and noisy channels, the resulting coders
outperform most of the source–channel coders reported in the
literature. More importantly, our coders are substantially more
robust than all previously reported source–channel coders with
respect to varying channel error conditions. This is a desired
feature in low-bandwidth wireless applications.
RAR 273 кбайт
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| Zixiang Xiong, Kannan Ramchandran, Michael T. Orchard, and Ya-Qin Zhang |
A Comparative Study of DCT- and Wavelet-Based Image Coding |
Abstract—We undertake a study of the performance difference
of the discrete cosine transform (DCT) and the wavelet transform
for both image and video coding, while comparing other aspects
of the coding system on an equal footing based on the state-of-theart
coding techniques. Our studies reveal that, for still images, the
wavelet transform outperforms the DCT typically by the order
of about 1 dB in peak signal-to-noise ratio. For video coding,
the advantage of wavelet schemes is less obvious. We believe that
the image and video compression algorithm should be addressed
from the overall system viewpoint: quantization, entropy coding,
and the complex interplay among elements of the coding system
are more important than spending all the efforts on optimizing
the transform.
RAR 66 кбайт
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| Nam Chul Kim, Ick Hoon Jang, Dae Ho Kim, and Won Hak Hong |
Reduction of Blocking Artifact in Block-Coded Images Using Wavelet Transform |
Abstract— We propose a simple yet efficient method which
reduces the blocking artifact in block-coded images by using
a wavelet transform. An image is considered a set of onedimensional
signals, and so all processings including the wavelet
transform are one-dimensionally executed. The artifact reduction
operation is applied to only the neighborhood of each block
boundary in the wavelet transform at the first and second scales.
The key idea behind the method is to remove the blocking
component which reveals stepwise discontinuities at block boundaries.
Each block boundary is classified into one of shade region,
smooth edge region, and step edge region. Threshold values for
the classification are selected adaptively according to each coded
image. The performance is evaluated for 512 . 512 images JPEG
coded with 30 : 1 and 40 : 1 compression ratios. Experimental
results show that the proposed method yields not only a PSNR
improvement of about 0.69–1.06 dB, but also subjective quality
nearly free of the blocking artifact and edge blur.
RAR 165 кбайт
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| Seongman Kim, Seunghyeon Rhee,Jun Geun Jeon, and Kyu Tae Park |
Interframe Coding Using Two-Stage Variable Block-Size Multiresolution otion
Estimation and Wavelet Decomposition |
Abstract— In this paper, we propose a two-stage variable
block-size multiresolution motion estimation (MRME) algorithm.
In this algorithm, a method to reduce the amount of motion
information is developed, and a bit allocation method minimizing
the sum of the motion information and the prediction error is
obtained in the wavelet transform domain.
In the first stage of the proposed scheme, we utilize a set of
wavelet components of the four subbands in the lowest resolution.
These motion vectors are directly used as motion vectors for the
lowest subband, and are scaled into the initial biases for other
subbands at every layer of the wavelet pyramid. In the second
stage, the bottom-up construction of a quadtree based on the
merge operation is performed. The proposed scheme reduces the
uncompressed bit rate of 8 bits/pixel into 0.212 bits/pixel at 41.1
dB of PSNR for the “Claire” sequence, which can be regarded as
nearly an 11% decrease compared with the conventional method.
RAR 378 кбайт
|
?
|
| Ricardo de Queiroz, C. K. Choi, Young Huh, and K. R. Rao |
Wavelet Transforms in a JPEG-Like Image Coder |
Abstract—The discrete wavelet transform (DWT) is incorporated into
the JPEG baseline system for image coding. The discrete cosine transform
(DCT) is replaced by an association of two-channel filter banks
connected hierarchically. JPEG block-scanning and quantization schemes
are adopted while we use JPEG’s entropy coder. The changes in scanning
can be incorporated into the transform block in such a way that the
only part that needs to be changed in a JPEG framework is to replace
the DCT by the DWT. Objective results and reconstructed images are
presented demonstrating that the proposed coder outperforms JPEG and
approaches the performance of more sophisticated and complex wavelet
coders. However, it does not require full-image buffering nor imposes a
large complexity increase.
RAR 542 кбайт
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?
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| Hiroyuki Katata, Norio Ito, Tomoko Aono and Hiroshi Kusao |
Object Wavelet Transform for Coding of Arbitrarily Shaped Image Segments |
Abstract— In this paper, one approach to transform an arbitrary
shapedimage region is addressed. The proposed approach called objectbased
wavelet transform (OWT), is simple to implement and a smooth
extension of regular wavelet transform (WT). OWT consist of two phase
processes: one is an extrapolation phase for regular WT and the other is
a handling coefficients phase for eliminating redundancy caused by the
extrapolation. Due to some experimental results, it is confirmed that the
method performs the same as other shape-adaptive approaches with low
complexity.
RAR 167 кбайт
|
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| Stephen A. Martucci, Iraj Sodagar, Tihao Chiang,and Ya-Qin Zhang |
A Zerotree Wavelet Video Coder |
Abstract—This paper describes a hybrid motion-compensated
wavelet transform coder designed for encoding video at very low
bit rates. The coder and its components have been submitted to
MPEG-4 to support the functionalities of compression efficiency
and scalability. Novel features of this coder are the use of
overlapping block motion compensation in combination with a
discrete wavelet transform followed by adaptive quantization and
zerotree entropy coding, plus rate control. The coder outperforms
the VM of MPEG-4 for coding of I-frames and matches the
performance of the VM for P-frames while providing a path to
spatial scalability, object scalability, and bitstream scalability.
RAR 463 кбайт
|
?
|
| Jiebo Luo, Chang Wen Chen, Kevin J. Parker, Fellow,and Thomas S. Huang, Fellow |
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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