Optimal block cosine transform image coding for noisy channels

The two dimensional block transform coding scheme based on the discrete cosine transform was studied extensively for image coding applications. While this scheme has proven to be efficient in the absence of channel errors, its performance degrades rapidly over noisy channels. A method is presented for the joint source channel coding optimization of a scheme based on the 2-D block cosine transform when the output of the encoder is to be transmitted via a memoryless design of the quantizers used for encoding the transform coefficients. This algorithm produces a set of locally optimum quantizers and the corresponding binary code assignment for the assumed transform coefficient statistics. To determine the optimum bit assignment among the transform coefficients, an algorithm was used based on the steepest descent method, which under certain convexity conditions on the performance of the channel optimized quantizers, yields the optimal bit allocation. Comprehensive simulation results for the performance of this locally optimum system over noisy channels were obtained and appropriate comparisons against a reference system designed for no channel error were rendered

A user's guide for the signal processing software for image and speech compression developed in the Communications and Signal Processing Laboratory (CSPL), version 1

A complete documentation of the software developed in the Communication and Signal Processing Laboratory (CSPL) during the period of July 1985 to March 1986 is provided. Utility programs and subroutines that were developed for a user-friendly image and speech processing environment are described. Additional programs for data compression of image and speech type signals are included. Also, programs for the zero-memory and block transform quantization in the presence of channel noise are described. Finally, several routines for simulating the perfromance of image compression algorithms are included

Robust vector quantization for noisy channels

The paper briefly discusses techniques for making vector quantizers more tolerant to tranmsission errors. Two algorithms are presented for obtaining an efficient binary word assignment to the vector quantizer codewords without increasing the transmission rate. It is shown that about 4.5 dB gain over random assignment can be achieved with these algorithms. It is also proposed to reduce the effects of error propagation in vector-predictive quantizers by appropriately constraining the response of the predictive loop. The constrained system is shown to have about 4 dB of SNR gain over an unconstrained system in a noisy channel, with a small loss of clean-channel performance

Mailed Outreach Invitations Significantly Improve HCC Surveillance Rates in Patients With Cirrhosis: A Randomized Clinical Trial

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147207/1/hep30129-sup-0001-Appendix.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147207/2/hep30129_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147207/3/hep30129.pd

An anti-TNF--α antibody mimetic to treat ocular inflammation

Infliximab is an antibody that neutralizes TNF-α and is used principally by systemic administration to treat many inflammatory disorders. We prepared the antibody mimetic Fab-PEG-Fab (FpFinfliximab) for direct intravitreal injection to assess whether such formulations have biological activity and potential utility for ocular use. FpFinfliximab was designed to address side effects caused by antibody degradation and the presence of the Fc region. Surface plasmon resonance analysis indicated that infliximab and FpFinfliximab maintained binding affinity for both human and murine recombinant TNF-α. No Fc mediated RPE cellular uptake was observed for FpFinfliximab. Both Infliximab and FpFinfliximab suppressed ocular inflammation by reducing the number of CD45+ infiltrate cells in the EAU mice model after a single intravitreal injection at the onset of peak disease. These results offer an opportunity to develop and formulate for ocular use, FpF molecules designed for single and potentially multiple targets using bi-specific FpFs

Subband Image Coding Using Entropy-Coded Quantization Over Noisy Channels.

In the first part of this paper, under the assumption of noiseless transmission, we develop two entropy-coded subband image coding schemes. The difference between these schemes is the procedure used for encoding the lowest frequency subband: predictive coding is used in one system and transform coding in the other. Other subbands are encoded using zero-memory quantization. After a careful study of subband statistics, the quantization parameters the corresponding Huffman codes and the bit allocation among subbands are all optimized. It is shown that both schemes perform considerably better than the scheme developed by Woods and O'Neil [2]. Roughly speaking, these new schemes perform the same as that in [2] at half the encoding rate. In the second part of the paper, after demonstrating the unacceptable sensitivity of these schemes to transmission noise, we will develop a combined source/channel coding scheme in which rate-compatible convolutional codes are used to provide protection agains channel noise. A packetization scheme to prevent infinite error propagation is used and an algorithm for optimal assignment of bits between the source and channel encoders of different subbands is developed. We will show the in the presence of channel noise, these channeloptimized schemes offer dramatic performance improvements ove' the schemes designed based on a noiseless channel assumption; they also perform better than that of [2] even in th absence of channel noise. Finally, the robustness of the proposed schemes against channel mismatch will be studied

Quantizer Design in LSP Speech Analysis-Synthesis.

The LSP speech analysis-synthesis method is known as one the most efficient vocoders. An important issue in encoding of the LSP parameters is that a certain ordering relationship between the LSP parameters is required to insure the stability of the synthesis filter. This requirement has an important impact on the design of quantizers for the LSP parameters. In this paper, the performance of several algorithms for the quantization of the LSP parameters is studied. A new adaptive method which utilizes the ordering property of the LSP parameters is presented. A combination of this adaptive algorithm with non-uniform step size quantization is shown to be a very effective method for encoding the LSP parameters. The performance of the different quantization schemes is studied on a long sequence of speech samples. For the spectral distortion measure, appropriate performance comparisons between the different quantization schemes are rendered

Optimal Detection of Discrete Markov Sources Over Discrete Memoryless Channels - Applications to Combined Sources-Channel Coding

We consider the problem of detecting a discrete Markov source which is transmitted across a discrete memoryless channel. The detection is based upon the maximum a posteriori (MAP) criterion which yields the minimum probability of error for a given observation. Two formulations of this problem are considered: (i) a sequence MAP detection in which the objective is to determine the most probable transmitted sequence given the observed sequence and (ii) an instantaneous MAP detection which is to determine the most probable transmitted symbol at time n given all the observations prior to and including time n. The solution to the first problem results in a "Viterbi-like" implementation of the MAP detector (with large delay) while the later problem results in a recursive (with no delay). For the special case of the binary symmetric Markov source and binary symmetric channel, simulation results are presented and an analysis of these two systems yields explicit critical channel bit error rates above which the MAP detectors become useful.Applications of the MAP detection problem in a combined source-channel coding system are considered. Here it is assumed that the source is highly correlated and that the source encoder (in our case, a vector quantizer (VQ) fails to remove all of the source redundancy. The remaining redundancy at the output of the source encoder is referred to as the "residual" redundancy. It is shown, through simulation, that the residual redundancy can be used by the MAP detectors to combat channel errors. For small block sizes, the proposed system beats Farvardin and Vaishampayan's channel- optimized VQ by wide margins. Finally, it is shown that the instantaneous MAP detector can be combined with the VQ decoder to form a minimum mean-squared error decoder. Simulation results are also given for this case