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51
Perceptual Coding of Digital Audio
 Proceedings of the IEEE
, 2000
"... During the last decade, CDquality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applic ..."
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Cited by 156 (3 self)
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During the last decade, CDquality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applications have created a demand for highquality digital audio delivery at low bit rates. In response to this need, considerable research has been devoted to the development of algorithms for perceptually transparent coding of highfidelity (CDquality) digital audio. As a result, many algorithms have been proposed, and several have now become international and/or commercial product standards. This paper reviews algorithms for perceptually transparent coding of CDquality digital audio, including both research and standardization activities. The paper is organized as follows. First, psychoacoustic principles are described with the MPEG psychoacoustic signal analysis model 1 discussed in some detail. Next, filter bank design issues and algorithms are addressed, with a particular emphasis placed on the Modified Discrete Cosine Transform (MDCT), a perfect reconstruction (PR) cosinemodulated filter bank that has become of central importance in perceptual audio coding. Then, we review methodologies that achieve perceptually transparent coding of FM and CDquality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction (LP) parameters, as well as hybrid algorithms that make use of more than one signal model. These discussions concentrate on architectures and applications of
A Modulated Complex Lapped Transform and its Applications to Audio Processing
 International Conference on Acoustics, Speech, and Signal Processing
, 1999
"... This paper introduces a new structure for a modulated complex lapped transform (MCLT), which is a complex extension of the modulated lapped transform (MLT). The MCLT is a particular kind of a 2x oversampled generalized DFT filter bank, whose real part corresponds to the MLT. That property can be use ..."
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Cited by 78 (12 self)
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This paper introduces a new structure for a modulated complex lapped transform (MCLT), which is a complex extension of the modulated lapped transform (MLT). The MCLT is a particular kind of a 2x oversampled generalized DFT filter bank, whose real part corresponds to the MLT. That property can be used for efficient implementation of joint echo cancellation, noise reduction, and coding, for example. Fast algorithms for the MCLT are presented, as well as examples that show the good performance of the MCLT in noise reduction and echo cancellation.
A Progressive Transmission Image Coder Using Linear Phase Uniform Filterbanks as Block Transforms
, 1999
"... This paper presents a novel image coding scheme using Mchannel linear phase perfect reconstruction filterbanks (LPPRFB's) in the embedded zerotree wavelet (EZW) framework introduced by Shapiro [1]. The innovation here is to replace the EZW's dyadic wavelet transform by Mchannel unifor ..."
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Cited by 46 (22 self)
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This paper presents a novel image coding scheme using Mchannel linear phase perfect reconstruction filterbanks (LPPRFB's) in the embedded zerotree wavelet (EZW) framework introduced by Shapiro [1]. The innovation here is to replace the EZW's dyadic wavelet transform by Mchannel uniformband maximally decimated LPPRFB's, which offer finer frequency spectrum partitioning and higher energy compaction. The transform stage can now be implemented as a block transform which supports parallel processing mode and facilitates regionof interest coding/decoding. For hardware implementation, the transform boasts efficient lattice structures, which employ a minimal number of delay elements and are robust under the quantization of lattice coefficients. The resulted compression algorithm also retains all attractive properties of the EZW coder and its variations such as progressive image transmission, embedded quantization, exact bit rate control, and idempotency. Despite its simplicity, our new coder outperforms some of the best image coders published recently in literature [1][4], for almost all test images (especially natural, hardtocode ones) at almost all bit rates.
Multilayered Image Representation: Application to Image Compression
 IEEE Trans. Image Process
"... Abstract The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. An image is parsed into a superposition of coherent layers: smoothregions layer, textures layer, etc. The multilayere ..."
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Cited by 40 (2 self)
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Abstract The main contribution of this work is a new paradigm for image representation and image compression. We describe a new multilayered representation technique for images. An image is parsed into a superposition of coherent layers: smoothregions layer, textures layer, etc. The multilayered decomposition algorithm consists in a cascade of compressions applied successively to the image itself and to the residuals that resulted from the previous compressions. During each iteration of the algorithm, we code the residual part in a lossy way: we only retain the most significant structures of the residual part, which results in a sparse representation. Each layer is encoded independently with a different transform, or basis, at a different bitrate; and the combination of the compressed layers can always be reconstructed in a meaningful way. The strength of the multilayer approach comes from the fact that different sets of basis functions complement each others: some of the basis functions will give reasonable account of the large trend of the data, while others will catch the local transients, or the oscillatory patterns. This multilayered representation has a lot of beautiful applications in image understanding, and image and video coding. We have implemented the algorithm and we have studied its capabilities.
Mchannel Linear Phase Perfect Reconstruction Filter Bank with Rational Coefficients
 IEEE Trans. on Signal Processing
, 1999
"... This paper introduces a general class of Mchannel linear phase perfect reconstruction lter banks with rational coefficients. A subset of the presented solutions has dyadic coefficients, leading to multiplierless implementations suitable for lowpower mobile computing. All of these filter banks are ..."
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Cited by 36 (19 self)
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This paper introduces a general class of Mchannel linear phase perfect reconstruction lter banks with rational coefficients. A subset of the presented solutions has dyadic coefficients, leading to multiplierless implementations suitable for lowpower mobile computing. All of these filter banks are constructed from a lattice structure that is VLSIfriendly, employs the minimum number of delay elements, and robustly enforces both linear phase and perfect reconstruction properties. The lattice coefficients are parameterized as a series of zeroorder lifting steps, providing fast, efficient, inplace computation of the subband coefficients. Despite the tight rational or integer constraint, image coding experiments show that these novel filter banks are very competitive with current popular transforms such as the 8 8 discrete cosine transform and the wavelet transform with 9=7tap biorthogonal irrationalcoefficient filters.
Fast progressive image coding without wavelets
 Data Compression Conference (DCC
, 2000
"... We introduce a new image compression algorithm that allows progressive image reconstruction – both in resolution and in fidelity, with a fully embedded bitstream. The algorithm is based on bitplane entropy coding of reordered transform coefficients, similar to the progressive wavelet codec (PWC) pr ..."
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Cited by 29 (2 self)
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We introduce a new image compression algorithm that allows progressive image reconstruction – both in resolution and in fidelity, with a fully embedded bitstream. The algorithm is based on bitplane entropy coding of reordered transform coefficients, similar to the progressive wavelet codec (PWC) previously introduced. Unlike PWC, however, our new progressive transform coder (PTC) does not use wavelets; it performs the spacefrequency decomposition step via a new lapped biorthogonal transform (LBT). PTC achieves a rate vs. distortion performance that is comparable (within 2%) to that of the stateoftheart SPIHT (set partitioning in hierarchical trees) codec. However, thanks to the use of the LBT, the spacefrequency decomposition step in PTC reduces the number of multiplications per pixel by a factor of 2.7, and the number of additions by about 15%, when compared to the fastest possible implementation of the “9/7 ” wavelet transform via lifting. Furthermore, since most of the computation in the LBT is in fact performed by a DCT, our PTC codec can make full use of fast software and hardware modules for 1D and 2D DCTs. 1.
LinearPhase Perfect Reconstruction Filter Bank: Lattice Structure, Design, and Application in Image Coding
 IEEE Trans. Signal Processing
, 2000
"... A lattice structure for anchannel linearphase perfect reconstruction filter bank (LPPRFB) based on the singular value decomposition (SVD) is introduced. The lattice can be proven to use a minimal number of delay elements and to completely span a large class of LPPRFB's: All analysis and synth ..."
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Cited by 29 (8 self)
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A lattice structure for anchannel linearphase perfect reconstruction filter bank (LPPRFB) based on the singular value decomposition (SVD) is introduced. The lattice can be proven to use a minimal number of delay elements and to completely span a large class of LPPRFB's: All analysis and synthesis filters have the same FIR length, sharing the same center of symmetry. The lattice also structurally enforces both linearphase and perfect reconstruction properties, is capable of providing fast and efficient implementation, and avoids the costly matrix inversion problem in the optimization process. From a block transform perspective, the new lattice can be viewed as representing a family of generalized lapped biorthogonal transform (GLBT) with an arbitrary number of channels and arbitrarily large overlap. The relaxation of the orthogonal constraint allows the GLBT to have significantly different analysis and synthesis basis functions, which can then be tailored appropriately to fit a particular application. Several design examples are presented along with a highperformance GLBTbased progressive image coder to demonstrate the potential of the new transforms.
Blocking Artifact Detection and Reduction in Compressed Data
 IEEE TRANSACTIONS ON CIRCUIT AND SYSTEMS FOR VIDEO TECHNOLOGY
, 2002
"... A novel frequencydomain technique for image blocking artifact detection and reduction is presented in this paper. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quant ..."
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Cited by 14 (1 self)
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A novel frequencydomain technique for image blocking artifact detection and reduction is presented in this paper. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the discrete cosine transform (DCT) coefficients are modeled by a Laplacian probability function. Then, for each block affected by blocking artifacts, its dc and ac coefficients are recalculated for artifact reduction. To achieve this, a closedform representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subbandlike representation of the DCT transform and is constrained by the quantization upper and lower bound. Experimental results illustrating the performance of the proposed method are presented and evaluated.
SPARSE ORTHONORMAL TRANSFORMS FOR IMAGE COMPRESSION
"... We propose a blockbased transform optimization and associated image compression technique that exploits regularity along directional image singularities. Unlike established work, directionality comes about as a byproduct of the proposed optimization rather than a built in constraint. Our work class ..."
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Cited by 14 (2 self)
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We propose a blockbased transform optimization and associated image compression technique that exploits regularity along directional image singularities. Unlike established work, directionality comes about as a byproduct of the proposed optimization rather than a built in constraint. Our work classifies image blocks and uses transforms that are optimal for each class, thereby decomposing image information into classification and transform coefficient information. The transforms are optimized using a set of training images. Our algebraic framework allows straightforward extension to nonblock transforms, allowing us to also design sparse lapped transforms that exploit geometric regularity. We use an EZW/SPIHT like entropy coder to encode the transform coefficients to show that our block and lapped designs have competitive ratedistortion performance. Our work can be seen as nonlinear approximation optimized transform coding of images subject to structural constraints on transform basis functions. Index Terms — Sparse orthonormal transforms, sparse lapped transforms, image coding, directional transforms 1.