The Experts below are selected from a list of 147 Experts worldwide ranked by ideXlab platform
Amir Said - One of the best experts on this subject based on the ideXlab platform.
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image wavelet coding systems part ii of set partition coding and image wavelet coding systems
Foundations and Trends in Signal Processing, 2008Co-Authors: William A Pearlman, Amir SaidAbstract:This monograph describes current-day wavelet transform image coding systems. As in the first part, steps of the algorithms are explained thoroughly and set apart. An image coding system consists of several stages: transformation, quantization, set partition or adaptive entropy coding or both, decoding including rate control, inverse transformation, de-quantization, and optional processing (see Figure 1.6). Wavelet transform systems can provide many desirable properties besides high efficiency, such as scalability in quality, scalability in resolution, and region-of-interest access to the coded bitstream. These properties are built into the JPEG2000 standard, so its coding will be fully described. Since JPEG2000 codes subBlocks of subbands, other methods, such as SBHP (Subband Block Hierarchical Partitioning) [3] and EZBC (Embedded Zero Block Coder) [8], that code subbands or its subBlocks independently are also described. The emphasis in this part is the use of the basic algorithms presented in the previous part in ways that achieve these desirable bitstream properties. In this vein, we describe a modification of the tree-based coding in SPIHT (Set Partitioning In Hierarchical Trees) [15], whose output bitstream can be decoded partially corresponding to a designated region of interest and is simultaneously quality and resolution scalable. This monograph is extracted and adapted from the forthcoming textbook entitled Digital Signal Compression: Principles and Practice by William A. Pearlman and Amir Said, Cambridge University Press, 2009.
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set partition coding part i of set partition coding and image wavelet coding systems
Foundations and Trends in Signal Processing, 2008Co-Authors: William A Pearlman, Amir SaidAbstract:The purpose of this two-part monograph is to present a tutorial on set partition coding, with emphasis and examples on image wavelet transform coding systems, and describe their use in modern image coding systems. Set partition coding is a procedure that recursively splits groups of integer data or transform elements guided by a sequence of threshold tests, producing groups of elements whose magnitudes are between two known thresholds, therefore, setting the maximum number of bits required for their binary representation. It produces groups of elements whose magnitudes are less than a certain known threshold. Therefore, the number of bits for representing an element in a particular group is no more than the base-2 logarithm of its threshold rounded up to the nearest integer. SPIHT (Set Partitioning in Hierarchical Trees) and SPECK (Set Partitioning Embedded Block) are popular state-of-the-art image Coders that use set partition coding as the primary entropy coding method. JPEG2000 and EZW (Embedded Zerotree Wavelet) use it in an auxiliary manner. Part I elucidates the fundamentals of set partition coding and explains the setting of thresholds and the Block and tree modes of partitioning. Algorithms are presented for the techniques of AGP (Amplitude and Group Partitioning), SPIHT, SPECK, and EZW. Numerical examples are worked out in detail for the latter three techniques. Part II describes various wavelet image coding systems that use set partitioning primarily, such as SBHP (Subband Block Hierarchical Partitioning), SPIHT, and EZBC (Embedded Zero-Block Coder). The basic JPEG2000 Coder is also described. The coding procedures and the specific methods are presented both logically and in algorithmic form, where possible. Besides the obvious objective of obtaining small file sizes, much emphasis is placed on achieving low computational complexity and desirable output bitstream attributes, such as embeddedness, scalability in resolution, and random access decodability. This monograph is extracted and adapted from the forthcoming textbook entitled Digital Signal Compression: Principles and Practice by William A. Pearlman and Amir Said, Cambridge University Press, 2009.
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efficient low complexity image coding with a set partitioning embedded Block Coder
IEEE Transactions on Circuits and Systems for Video Technology, 2004Co-Authors: William A Pearlman, Asad Islam, Nithin Nagaraj, Amir SaidAbstract:We propose an embedded, Block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect to thresholds that are integer powers of two. It exploits two fundamental characteristics of an image transform-the well-defined hierarchical structure, and energy clustering in frequency and in space. The two partition strategies allow for versatile and efficient coding of several image transform structures, including dyadic, Blocks inside subbands, wavelet packets, and discrete cosine transform (DCT). We describe the use of this coding algorithm in several implementations, including reversible (lossless) coding and its adaptation for color images, and show extensive comparisons with other state-of-the-art Coders, such as set partitioning in hierarchical trees (SPIHT) and JPEG2000. We conclude that this algorithm, in addition to being very flexible, retains all the desirable features of these algorithms and is highly competitive to them in compression efficiency.
Farid Ghani - One of the best experts on this subject based on the ideXlab platform.
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ICME - Memory efficient image coding with embedded zero Block-tree Coder
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004Co-Authors: H. Arora, Ekram Khan, P Singh, Farid GhaniAbstract:The work presents an embedded and memory efficient image compression algorithm which exploits both inter- and intra-band correlation of wavelet coefficients. Set partitioning in hierarchical tree (SPIHT) is a zero-tree based Coder which exploits inter-band correlation among bands of the same orientation, while the set-partitioning embedded Block Coder (SPECK) is a zero-Block based Coder which exploits intra-band correlation. However, they have extensively large memory requirements due to the use of three/two linked lists whose entries increase from one-bit-plane to the next. We propose an algorithm that is based on Block-set partitioning and quad-splitting using two re-usable lists. The main list is initialized at the beginning of each bit-plane and is exhausted within the same bit-plane. This makes our proposed algorithm highly memory efficient. Experimental results show that the compression efficiency of the proposed method is comparable to any state-of-the-art image Coder while reducing the memory requirement by 50-60% in comparison to the SPIHT algorithm.
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memory efficient image coding with embedded zero Block tree Coder
International Conference on Multimedia and Expo, 2004Co-Authors: H. Arora, Ekram Khan, P Singh, Farid GhaniAbstract:The work presents an embedded and memory efficient image compression algorithm which exploits both inter- and intra-band correlation of wavelet coefficients. Set partitioning in hierarchical tree (SPIHT) is a zero-tree based Coder which exploits inter-band correlation among bands of the same orientation, while the set-partitioning embedded Block Coder (SPECK) is a zero-Block based Coder which exploits intra-band correlation. However, they have extensively large memory requirements due to the use of three/two linked lists whose entries increase from one-bit-plane to the next. We propose an algorithm that is based on Block-set partitioning and quad-splitting using two re-usable lists. The main list is initialized at the beginning of each bit-plane and is exhausted within the same bit-plane. This makes our proposed algorithm highly memory efficient. Experimental results show that the compression efficiency of the proposed method is comparable to any state-of-the-art image Coder while reducing the memory requirement by 50-60% in comparison to the SPIHT algorithm.
William A Pearlman - One of the best experts on this subject based on the ideXlab platform.
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image wavelet coding systems part ii of set partition coding and image wavelet coding systems
Foundations and Trends in Signal Processing, 2008Co-Authors: William A Pearlman, Amir SaidAbstract:This monograph describes current-day wavelet transform image coding systems. As in the first part, steps of the algorithms are explained thoroughly and set apart. An image coding system consists of several stages: transformation, quantization, set partition or adaptive entropy coding or both, decoding including rate control, inverse transformation, de-quantization, and optional processing (see Figure 1.6). Wavelet transform systems can provide many desirable properties besides high efficiency, such as scalability in quality, scalability in resolution, and region-of-interest access to the coded bitstream. These properties are built into the JPEG2000 standard, so its coding will be fully described. Since JPEG2000 codes subBlocks of subbands, other methods, such as SBHP (Subband Block Hierarchical Partitioning) [3] and EZBC (Embedded Zero Block Coder) [8], that code subbands or its subBlocks independently are also described. The emphasis in this part is the use of the basic algorithms presented in the previous part in ways that achieve these desirable bitstream properties. In this vein, we describe a modification of the tree-based coding in SPIHT (Set Partitioning In Hierarchical Trees) [15], whose output bitstream can be decoded partially corresponding to a designated region of interest and is simultaneously quality and resolution scalable. This monograph is extracted and adapted from the forthcoming textbook entitled Digital Signal Compression: Principles and Practice by William A. Pearlman and Amir Said, Cambridge University Press, 2009.
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set partition coding part i of set partition coding and image wavelet coding systems
Foundations and Trends in Signal Processing, 2008Co-Authors: William A Pearlman, Amir SaidAbstract:The purpose of this two-part monograph is to present a tutorial on set partition coding, with emphasis and examples on image wavelet transform coding systems, and describe their use in modern image coding systems. Set partition coding is a procedure that recursively splits groups of integer data or transform elements guided by a sequence of threshold tests, producing groups of elements whose magnitudes are between two known thresholds, therefore, setting the maximum number of bits required for their binary representation. It produces groups of elements whose magnitudes are less than a certain known threshold. Therefore, the number of bits for representing an element in a particular group is no more than the base-2 logarithm of its threshold rounded up to the nearest integer. SPIHT (Set Partitioning in Hierarchical Trees) and SPECK (Set Partitioning Embedded Block) are popular state-of-the-art image Coders that use set partition coding as the primary entropy coding method. JPEG2000 and EZW (Embedded Zerotree Wavelet) use it in an auxiliary manner. Part I elucidates the fundamentals of set partition coding and explains the setting of thresholds and the Block and tree modes of partitioning. Algorithms are presented for the techniques of AGP (Amplitude and Group Partitioning), SPIHT, SPECK, and EZW. Numerical examples are worked out in detail for the latter three techniques. Part II describes various wavelet image coding systems that use set partitioning primarily, such as SBHP (Subband Block Hierarchical Partitioning), SPIHT, and EZBC (Embedded Zero-Block Coder). The basic JPEG2000 Coder is also described. The coding procedures and the specific methods are presented both logically and in algorithmic form, where possible. Besides the obvious objective of obtaining small file sizes, much emphasis is placed on achieving low computational complexity and desirable output bitstream attributes, such as embeddedness, scalability in resolution, and random access decodability. This monograph is extracted and adapted from the forthcoming textbook entitled Digital Signal Compression: Principles and Practice by William A. Pearlman and Amir Said, Cambridge University Press, 2009.
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efficient low complexity image coding with a set partitioning embedded Block Coder
IEEE Transactions on Circuits and Systems for Video Technology, 2004Co-Authors: William A Pearlman, Asad Islam, Nithin Nagaraj, Amir SaidAbstract:We propose an embedded, Block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect to thresholds that are integer powers of two. It exploits two fundamental characteristics of an image transform-the well-defined hierarchical structure, and energy clustering in frequency and in space. The two partition strategies allow for versatile and efficient coding of several image transform structures, including dyadic, Blocks inside subbands, wavelet packets, and discrete cosine transform (DCT). We describe the use of this coding algorithm in several implementations, including reversible (lossless) coding and its adaptation for color images, and show extensive comparisons with other state-of-the-art Coders, such as set partitioning in hierarchical trees (SPIHT) and JPEG2000. We conclude that this algorithm, in addition to being very flexible, retains all the desirable features of these algorithms and is highly competitive to them in compression efficiency.
H. Arora - One of the best experts on this subject based on the ideXlab platform.
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ICME - Memory efficient image coding with embedded zero Block-tree Coder
2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), 2004Co-Authors: H. Arora, Ekram Khan, P Singh, Farid GhaniAbstract:The work presents an embedded and memory efficient image compression algorithm which exploits both inter- and intra-band correlation of wavelet coefficients. Set partitioning in hierarchical tree (SPIHT) is a zero-tree based Coder which exploits inter-band correlation among bands of the same orientation, while the set-partitioning embedded Block Coder (SPECK) is a zero-Block based Coder which exploits intra-band correlation. However, they have extensively large memory requirements due to the use of three/two linked lists whose entries increase from one-bit-plane to the next. We propose an algorithm that is based on Block-set partitioning and quad-splitting using two re-usable lists. The main list is initialized at the beginning of each bit-plane and is exhausted within the same bit-plane. This makes our proposed algorithm highly memory efficient. Experimental results show that the compression efficiency of the proposed method is comparable to any state-of-the-art image Coder while reducing the memory requirement by 50-60% in comparison to the SPIHT algorithm.
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memory efficient image coding with embedded zero Block tree Coder
International Conference on Multimedia and Expo, 2004Co-Authors: H. Arora, Ekram Khan, P Singh, Farid GhaniAbstract:The work presents an embedded and memory efficient image compression algorithm which exploits both inter- and intra-band correlation of wavelet coefficients. Set partitioning in hierarchical tree (SPIHT) is a zero-tree based Coder which exploits inter-band correlation among bands of the same orientation, while the set-partitioning embedded Block Coder (SPECK) is a zero-Block based Coder which exploits intra-band correlation. However, they have extensively large memory requirements due to the use of three/two linked lists whose entries increase from one-bit-plane to the next. We propose an algorithm that is based on Block-set partitioning and quad-splitting using two re-usable lists. The main list is initialized at the beginning of each bit-plane and is exhausted within the same bit-plane. This makes our proposed algorithm highly memory efficient. Experimental results show that the compression efficiency of the proposed method is comparable to any state-of-the-art image Coder while reducing the memory requirement by 50-60% in comparison to the SPIHT algorithm.
Gene H Golub - One of the best experts on this subject based on the ideXlab platform.
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variable projection for near optimal filtering in low bit rate Block Coders
IEEE Transactions on Circuits and Systems for Video Technology, 2005Co-Authors: Y Tsaig, Michael Elad, Peyman Milanfar, Gene H GolubAbstract:Recent work on Block-based compression for low bit-rate coding has shown that employing a Block Coder within a sampling scheme where the image is downsampled prior to coding (and upsampled after the decoding stage) results in superior performance compared to standard Block coding. We explore the use of optimal decimation and interpolation filters in this coding scheme. We show that the problem of finding optimal filters for a general, unknown, "black-box" Coder can be written as a separable least squares problem in two sets of variables. We then elegantly solve this optimization problem using the Variable Projection method. The experimental results presented clearly exhibit a significant improvement over existing approaches.