This paper proposes code for secure distributed storage systems (sDSS), called secure shortened product-matrix minimum storage regenerating code (PM-ssMSR). The code uses shortening technique applied on the mother code, product-matrix minimum storage regenerating (PM-MSR), for maintenance of the DSS efficiency and two different methods for achieving the system security. The first method enables by performing random keys technique on the PM-ssMSR code to guarantee perfectly secure system. Hence, the second technique using an outer code achieves weakly secure system. For both cases, their information-theoretic secrecy is proven and is shown that the weakly secure code achieve higher secrecy bandwidth than the perfectly secure code.

A vector permutation code (VPC) is defined as a fixed-rate vector quantizer with a codebook C[y/sub 0/] = {y/sub l/}/sub l=0//sup M=1/ consisting of all the distinct permutations of the columns of an L/spl times/n matrix y/sub 0/=(/spl mu//sub 1/,...., /spl mu//sub 1/, ................, /spl mu//sub k/,....., /spl mu//sub k/), where each L-dimensional column-vector /spl mu//sub i/ appears n/sub i/ times. Based on error criterion vector quantisation an algorithm for composition vector design is also proposed.

Very efficient still image compression methods exist today, but most of them are not fit for transmission on error prone channels. One example of state-of-the-art robust image coding is the use of wavelet compression followed by forward error correction, as proposed by P.G. Sherwood and K. Zeger (see Proc. IEEE Data Compress. Conf, p.72-81, 1997); that method gives high coding efficiency but suffers from the incomplete decoding problem. An alternative to this is the joint source/channel coding scheme by Qing Chen and T.R. Fischer (see IEEE Trans. on Img. Proc., vol.7, no.4, p.496-505, 1998), which overcomes this difficulty at the expense of a lower PSNR performance. We propose a joint source/channel coding scheme with increased coding efficiency based on lapped transforms with block classification. With an efficient bit allocation strategy and the high coding gain of lapped transforms, a significant increase, of the order of 2 dB, in the PSNR versus BER performance has been obtained over the classical joint source/channel scheme on binary symmetric channels.

A scheme for the efficient transmission of remote sensing satellite (RSS) images is presented in this paper. A joint source/channel coding solution was used to deal with the conflicting issues of data compression, to reduce the amount of data to be sent, and data protection, to cope with errors introduced by the channel, issues further burdened by the stringent low complexity and low memory requirements brought by the need for on-board compression. Results are presented showing that the proposed scheme attains good PSNR performance while still maintaining low complexity.

A method that reduces the blocking effect of the lossy compression of multi-dimensional data with a version of the UMMP (universal multi-scale matching pursuit) algorithm is presented. The method consists of a generalization of the previously presented UMMP which extends its use to bases with overlapping functions. We circumvent the difficult optimization problem posed by overlapping functions at the encoder by the use of two different bases, one for the encoding and another, with overlapping functions, for the decoding of the data.

Summary form only given. One of the most popular encoders in the literature is the LZ78, which was proposed by Ziv and Lempel (1978). We establish a recursive way to find the longest m-tuple match. We prove the following theorem that shows how to obtain a longest (m+1)-tuple match from the longest m-tuple match. It shows that a (m+1)-tuple match is the concatenation of the first (m-1) words of the m-tuple match with the next longest double match. Therefore, the longest (m+1)-tuple match can be found using the m-tuple match and a procedure to compute the longest double match. Our theorem is as follows. Let A be a source alphabet, let A* be the set of all finite strings of A, and D/spl sub/A*, such that if x/spl isin/D then all prefixes of x belong to D. Let u denote a one-sided infinite sequence. If b/sub 1//sup m/ is the longest m-tuple match in u, with respect to D, then there is a longest (m+1)-tuple match b/spl circ//sub 1//sup m+1/, such that b/spl circ//sub i/=b/sub i/,/spl forall/i/spl isin/{1,...m-1}. We implemented the fast mmLZ and the results show a improvement in compression of around 5% over the LZW, in the Canterbury Corpus (Arnold and Bell, 1997) with little extra computational cost.

Two practical lossy Lempel-Ziv schemes are proposed in this paper. The schemes are suitable for compression of sources with large alphabet cardinality. Computer simulation results obtained by compressing the output of a Gaussian source revealed a performance comparable to the performance of scalar quantization followed by entropy coding. Compression of DCT transformed and subband decomposed images showed a performance close to the performance of JPEG compressed images.

A clustering algorithm for the design of efficient vector quantizers to be followed by entropy coding is proposed. The algorithm, called entropy-constrained pairwise nearest neighbor (ECPNN), designs codebooks by merging the pair of Voronoi regions which gives the least increase in distortion for a given decrease in entropy. The algorithm can be used as an alternative to the entropy-constrained vector quantizer design (ECVQ) proposed by Chou, Lookabaugh, and Gray (1989). By a natural extension of the ECPNN algorithm the authors develop another algorithm that designs alphabet and entropy-constrained vector quantizers and call it alphabet- and entropy-constrained pairwise nearest neighbor (AECPNN) design. Through simulations on synthetic sources, it is shown that ECPNN and ECVQ have indistinguishable mean-square-error versus rate performance and that the ECPNN and AECPNN algorithms obtain as close performance by the same measure as the ECVQ and AECVQ (Rao and Pearlman, 1993) algorithms. The advantages over ECVQ are that the ECPNN approach enables much faster codebook design and uses smaller codebooks. A single pass through the ECPNN (or AECPNN) design algorithm, which progresses from larger to successively smaller rates, allows the storage of any desired number of intermediate codebooks. In the context of multirate subband (or transform) coders, this feature is especially desirable. The performance of coding image pyramids using ECPNN and AECPNN codebooks at rates from 1/3 to 1.0 bit/pixel is discussed.>

Continuous phase modulation (CPM) signals may be precoded by a convolutional code followed by a mapping rule. Better codes may, however, be obtained by a more general code formed by a finite state machine. We provide improved codes for one such finite state machine. A new description of codes and design rules are presented. Better results are demonstrated for the partial response CPM schemes and previous optimum results are matched for the full response case. Tests of an M-algorithm decoder achieve near maximum-likelihood performance with only a fraction of the complexity of the Viterbi algorithm.>

A modified Lempel-Ziv (1978) algorithm (m-LZA) for data compaction is proposed. The performance of the m-LZA in terms of compaction rate is better than the performance of the LZA. The improvement realized by examining the tree structure underlying the LZA is corroborated by comparing the compaction rates obtained with the application of both algorithms to the compaction of image data. Another interesting feature of the m-LZA is its independence of the size of the source alphabet.>

A clustering algorithm for the design of efficient vector quantizers to be followed by entropy coding is proposed. The algorithm generates a sequence of quantizers whith rates close to theoretical rate- distortion bound. A fast version of this algorithm can be used as an alternative to the entropy-constrained vector quantizer technique proposed by Chou, Lookabaugh and Gray.

A rate-distortion theory is introduced for the optimal encoding of stationary memoryless continuous-amplitude sources with a single-letter distortion measure and reproduction alphabets of a given finite size. The theory arises from a judicious approximation of the original continuous-input discrete-output problem by one with discrete input and output. A size-constrained output alphabet rate-distortion function is defined, its coding significance is established by coding theorems, and a convergent algorithm is presented for its evaluation. The theory is applied to Gaussian sources with squared-error distortion measure. Using the algorithm for the calculation of the new rate-distortion function in this case establishes the existence of codes which attain almost any desired rate between the rate-distortion bound and the optimum entropy-coded quantizer. Furthermore, one can closely approach the rate-distortion limit with a surprisingly small number of output levels. The calculation furnishes optimal output levels, output level probabilities, and other parameters necessary for a trellis coding simulation. The search algorithm represents the first use for asymmetric sources and distortion measures of a variation of a single stack algorithm proposed by Gallager. Carrying out the simulation at a rate of 1 bit per source symbol, codes are found with 4 and 64 output levels which attain distortions smaller than that of an optimum quantizer and close to the rate-distortion bound. Furthermore, these codes attain comparable or better performance with far less search effort than previous attempts with a continuous output alphabet.