K. Sayood, Introduction to Data Compression, San Francisco, CA: Morgan-Kaufmann, 1996. Home/Search Document Not in Database Summary Related Articles Check |
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A Tutorial on Modern Lossy Wavelet Image Compression.. - Usevitch (2000) (5 citations) (Correct)
....as R k bits coefficient, such that # 1) An overall bit rate RR k = 4 1 4 (3) is satisfied and # 2) The reconstruction distortion is minimized. Since uniform scalar quantization is used, the distortion or error energy introduced by the quantizer in each subband can be modeled by [8] [ 9] ## # r k R y k k 222 , 4) where # y k is the variance of coefficients in each subband, R k is the subband bit rate, and# k is a parameter which depends on the probability distribution in the subbands (Gaussian or Laplacian or uniform, etc. Equation (4) makes intuitive sense since ....
....and (5) can be combined to form a constrained minimization problem which can be solved using Lagrange multipliers [29] where the Lagrangian to be minimized is JRR R y k = # # # ## ### 4 22 1 1 4 . Minimization of this function results in the best bit allocation of [8] [9] RR k yk k M yk = # 2 1 log . 6) Equation (6) is not valid in all cases, for example when it result in R k s that are negative, but methods have been derived to handle these technicalities [10] Using the optimal bit rates, the coefficients in each of the subbands are ....
K. Sayood, Introduction to Data Compression. San Mateo, CA: Morgan Kaufmann, 2000.
Group Testing for Image Compression - Hong (2000) (4 citations) (Correct)
....transform depends on the amount of energy compaction the transform achieves; higher energy compaction corresponds to fewer coefficients with large magnitude and more coefficients close to zero. There are many different measures of transform efficacy, but one widely used measure is the coding gain [46], which is a ratio of the arithmetic mean of the variances of the transform coefficients to their geometric means. It can be shown that the coding gain of a transform is an objective measure of the improvement in compression performance obtained when quantizing the transform coefficients instead ....
....sequence of symbols can occur. Encoding is achieved by choosing a number x in [a; b) and writing down its sufficiently long prefix of x (in binary) so that x is known to be in [a; b) Decoding is achieved by using x to identify [a; b) For more details on practical implementations, see [46]. See [59] for an example of an adaptive arithmetic code. It can be shown that the bit rate R(f; S) for any arithmetic code f on an i.i.d. source S satisfies R(f; S) H(S) 41 3.3.4 Golomb Codes Golomb codes were originally defined in [18] to compress sources that generate nonnegative integers ....
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K. Sayood. Introduction to Data Compression. Morgan Kaufmann Publishers, 1996.
High Rate Vector Quantization for Detection - Gupta, Hero, III (2003) (1 citation) (Correct)
....at points where #(#) and # # (#) are maximized, respectively. In Figures A.2.2, A.2.2, and A.2.2, the performances of scalar quantizers with the various optimal point densities are compared. The quantizers were obtained using the LBG algorithm, also known as the generalized Lloyd algorithm [1, 43,44], applied to the relevant point densities. The essence of this approach is as follows: The MSE optimal point density for source density #(#) is # (#) # (#) #(#) ##. Note that # is invertible and # (#) #(#) ##. Since the quantizer produced by the generalized 22 Lloyd ....
K. Sayood, Introduction to Data Compression, Morgan-Kaufman, 1996.
Improved Low Bit-Rate Audio Compression Using Reduced.. - Ben-Shalom, Werman.. (Correct)
....If we denote by ACBED F the average number of bits used to encode samples in the block, AHG the average bit rate used to encode samples in the k th band and by IJG the variance of the coefficients on the k th band. Then the optimal bit allocation for the different bands is given by [11]: A3G A BED F H K L G MNM W. I PO (10) The bit allocation according to equation 10 is optimal in terms of the reconstruction error. The problem is that ARG might be negative or not an integer number. To solve this problem we use an iterative algorithm for bit ....
....The bit allocation according to equation 10 is optimal in terms of the reconstruction error. The problem is that ARG might be negative or not an integer number. To solve this problem we use an iterative algorithm for bit allocation with positive integer constraint similar to the one described in [11]. Using the bit allocation information we quantize the ICA coefficients with a uniform quantizer. We assign 8 bits to quantize the ICA mixing matrix samples. In our experiments the dimension was chosen to 5. Thus, the ICA matrix size is S UT which results in overhead of 160 bits assigned for ....
K. Sayood, Introduction to Data Compression, Morgan Kaufmann Publishers, 1996.
Energy Aware Lossless Data Compression - Barr, Asanovic (2003) (9 citations) (Correct)
....of the Prediction by Partial Match (PPM) compressors that came before it. LZO represents an approach for achieving great speed with LZ77. Each of the five applications is summarized below assuming some familiarity with each algorithm. A more complete treatment with citations may be found in [36]. zlib combines LZ77 and Huffman coding to form an algorithm known as deflate. The LZ77 sliding window size and hash table memory size may be set by the user. LZ77 tries to replace a string of symbols with a pointer to the longest prefix match previously encountered. A larger window improves ....
K. Sayood. Introduction to data compression. Morgan Kaufman Publishers, second edition, 2002.
Reconstruction of Predictively Encoded Signals over Noisy.. - Lahouti, Khandani (2002) (Correct)
....for prediction. Alternatively, ARMA systems use both sets of data . For a comprehensive review of predictive In this work, the terms AR, MA and ARMA predictive systems indicate using either a linear or non linear predictive function, unless specifically specified. 4 quantization refer to [23][24] The output of a predictive coder is the index I n corresponding to the quantized prediction residue Y n . In a basic predictive decoder, the block labeled reverse mapping in Figure 1, is simply the inverse of the index generation function at the encoder, which ignores any residual ....
K. Sayood, Introduction to Data Compression, Morgan Kaufmann Publishers, Inc., San Francisco, CA, 2000.
Ecient Encoding and Rendering of Time-Varying Volume Data - Ma, Smith, Shih, Shen (1998) (1 citation) (Correct)
....studied and may be applied to new applications according to data characteristics and certain requirements. There are lossless and lossy compression methods. Popular compression techniques include hu#man coding, scalar vector quantization, di#erential encoding, subband coding, and transform coding [15]. Frequently, scientists demand lossless methods to preserve the accuracy of their original results. However, when performing data visualization, limited by the display technology and the implementation of rendering algorithms, degradation in image quality cannot be totally avoided. The questions ....
K. Sayood, Introduction to Data Compression, Morgan Kaufmann Publishers, Inc., 1996.
Visualization Techniques for Time-Varying Volume Data - Ma, Shen (Correct)
....and may be applied to new applications according to data characteristics and certain requirements. There are lossless and lossy compression methods. Popular compression tech niques include huffman coding, scalar vector quantization, differential encoding, subband coding, and transform coding [9]. Frequently, scientists demand lossless methods to preserve the accuracy of their original results. However, when performing data visualization, limited by the display technology and the implementation of rendering algorithms, degradation in image quality cannot be totally avoided. The questions ....
SAYOOD, K. Introduction to Data Compression. Morgan Kaufmann Publishers, Inc., 1996.
Study Of Mutual Information In Perceptual Coding With.. - Ben-Shalom, Werman.. (Correct)
....coefficients. If we denote by R avg the average number of bits used to encode samples in the block, R k the average bit rate used to encode samples in the k th band and by # k the variance of the coefficients on the k th band. Then the optimal bit allocation for the different bands is given by [11]: R k = R avg 1 (# (13) The bit allocation according to equation 13 is optimal in terms of the reconstruction error. The problem is that R k might be negative or not an integer number. To solve this problem we use an iterative algorithm for bit allocation with positive integer ....
....The bit allocation according to equation 13 is optimal in terms of the reconstruction error. The problem is that R k might be negative or not an integer number. To solve this problem we use an iterative algorithm for bit allocation with positive integer constraint similar to the one described in [11]. Using the bit allocation information we quantize the ICA coefficients with a uniform quantizer. We assign 8 bits to quantize the ICA mixing matrix samples. We compensate the overhead of the ICA matrix transmission with the dimension reduction of the filter bank coefficients. The scalefactors ....
K. Sayood, Introduction to Data Compression, Morgan Kaufmann Publishers, 1996.
Improving Fractal Image Compression Schemes Through.. - Ghazel, Khandani, Vrscay (Correct)
....of an appropriate vector 663 Figure 2: vs. 3 coefficients for scheme I, based on four test images( Lena , Ape , Aspen and San Francisco ) quantization algorithm to quantize the slope and the intercept simultaneously as a two dimensional vector. The LBG vector quantization algorithm [11] was used to quantize some of the highly correlated fractal coefficients simultaneously and dependently, and the results are illustrated in figures 3 and 4. The quantization details as well as the errors are given in the captions. 4. DISCUSSION AND CONCLUSION This paper represents a preliminary ....
K. Sayood, Introduction to Data Compression, Morgan Kaufman, San Francisco, CA, 1996.
Compressibility Characteristics of Address/Data Transfers in - Commercial Workloads.. (Correct)
....interest in compression in the main memory, several studies have examined compressibility of main memory data and specialized compression algorithms. Reference [12] studies compressibility of many popular Unix desktop applications using both the traditional algorithms (e.g. LZW, Arithmetic coding [20]) and the X RL algorithm invented by the authors [13] The latter algorithm encodes 4 bytes at a time using partial matching of bytes and dynamic coding based on a small dictionary. It is claimed to be especially suited for small block sizes and hardware implementation. The authors show that ....
K. Sayood, Introduction to Data Compression, 2nd edition, Morgan Kaufmann, 2000, chapter 5.
Compression with Fast Random Access - Rodler (Correct)
....is to obtain the best compression ratio while minimizing the distortion in the reconstructed images. Often this limits the random accessibility. The reason being that most compression schemes employ variable bitrate techniques such as Hu#man (used in JPEG [39] and MPEG [40] and Arithmetic coders [74], or di#erential encoders such as the Adaptive Di#erential Pulse Code coder [74] On the other hand, such methods provide fast sequential decoding which is important in, for example, compression of images and video sequences. However, techniques dealing with the issue of random access in ....
....reconstructed images. Often this limits the random accessibility. The reason being that most compression schemes employ variable bitrate techniques such as Hu#man (used in JPEG [39] and MPEG [40] and Arithmetic coders [74] or di#erential encoders such as the Adaptive Di#erential Pulse Code coder [74]. On the other hand, such methods provide fast sequential decoding which is important in, for example, compression of images and video sequences. However, techniques dealing with the issue of random access in volumetric data have been emerging. In [57, 58] Muraki introduced the idea of using ....
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Khalid Sayood. Introduction to Data Compression. Morgan Kaufmann Publishers, Inc., USA, 1996.
An Adaptive Dictionary Encoding Scheme for SOC Data Buses - Lv, Henkel, Lekatsas, Wolf (2002) (Correct)
....p= C (j) is the voltage swing on i th bus line during the j th transaction and N is the bit width of the bus. We use this model later on calculate the energy consumption on the data bus. 4 Encoding and Decoding Schemes Dictionary encoding techniques are widely used in data compression[12] (e.g. in UNIX compress, GIF image compression etc) Dictionary techniques target at data source properties like recurhng patterns that are kept in the so called dictiona when identified. Hence, a small number of symbols can represent a large pattern. However, if a pattern is not in the ....
K. Sayood, "Introduction to Data Compression", Morgan Kaufmann Publishers, 1996.
A Comparative Study on OCR Tools - Carlos De Mello (Correct)
....because some techniques used in OCR programs require inputs to be correctly aligned. The skew detector can determine the angle of rotation of a document image and use this information to reorient the image to align it. One way to perform skew detection is with the use of Hough transforms [3, 10]. This procedure can check if there is a rotation in the image and its angle, allowing the software to perform a new rotation to make the image straight. The number of words correctly transcribed Image rotation is treated only by Omnipage, Corel OCR Trace and Text Bridge Pro. These results are ....
K.Sayood. Introduction to Data Compression. Morgan Kauffman Publishers, Inc., 1996.
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K. Sayood, Introduction to Data Compression, San Francisco, CA: Morgan-Kaufmann, 1996.
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K. Sayood, "Introduction to Data Compression", Morgan-Kaufmann, San Francisco, CA, 1996.
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K. Sayood. Introduction to Data Compression. Morgan Kaufmann Publishers, Inc., 1996.
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K. Sayood. Introduction to Data Compression. Morgan Kaufmann Publishers, Inc., 1996.
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K. Sayood (1996). Introduction to Data Compression. Morgan Kaufmann, San Francisco.
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K. Sayood, Introduction to Data Compression. San Francisco: Morgan Kaufmann Publishers, Inc., 1996.
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