Ole Riis Jensen

Content layer progressive coding of digital maps

A new lossless context based method is presented for content progressive coding of limited bits/pixel images, such as maps, company logos, etc., common on the World Wide Web. Progressive encoding is achieved by encoding the image in content layers based on color level or other predefined information. Information from already coded layers are used when coding subsequent layers. This approach is combined with efficient template based context bilevel coding, context collapsing methods for multilevel images and arithmetic coding. Relative pixel patterns are used to collapse contexts. Expressions for calculating the resulting number of contexts are given. The new methods outperform existing schemes coding digital maps and in addition provide progressive coding. Compared to the state-of-the-art PWC coder, the compressed size is reduced to 50-70% on our layered map test images.

Forced sequence sequential decoding: a concatenated coding system with iterated sequential inner decoding

We describe a new concatenated decoding scheme based on iterations between an inner sequentially decoded convolutional code of rate R=1/4 and memory M=23, and block interleaved outer Reed-Solomon (RS) codes with nonuniform profile. With this scheme decoding with good performance is possible as low as E/sub b//N/sub 0/=0.6 dB, which is about 1.25 dB below the signal-to-noise ratio (SNR) that marks the cutoff rate for the full system. Accounting for about 0.45 dB due to the outer codes, sequential decoding takes place at about 1.7 dB below the SNR cutoff rate for the convolutional code. This is possible since the iteration process provides the sequential decoders with side information that allows a smaller average load and minimizes the probability of computational overflow. Analytical results for the probability that the first RS word is decoded after C computations are presented. These results are supported by simulation results that are also extended to other parameters.

Content progressive coding of limited bits/pixel images

We propose a new lossless context based method for content progressive coding of limited bits/pixel images, such as maps, company logos, etc., common on the WWW. Progressive encoding is done by separating the image into content layers based on color level or predefined information. By introducing skip-pixel coding the number of pixels encoded in each layer is reduced by using information from already coded layers. This approach is combined with efficient template based context bi-level coding, context collapsing methods for multi-level images and arithmetic coding. With our new method we are thus able to outperform existing coding schemes and in addition provide progressive coding. Compared to GIF we are able to reduce the compressed size with a factor of up to 3.

Virtual seminar room-modelling and experimentation in horizontal and vertical integration

The initial design considerations and research goals for an ATM network based virtual seminar room with five sites are presented. The basic observation behind the design of the virtual seminar room is, that besides the constant growth in available bandwidth for transmission in communication networks, many networks either already offer or are developing technologies to give quality of service (QoS) guarantees. This means that applications not only will support transmission of coded audio and video, but can be designed in a known and well-controlled network environment, which enables the applications to provide, in a broad sense, a high and reliable quality presented at the human computer interface.

Concatenated coding system with iterated sequential inner decoding

We describe a concatenated coding system with iterated sequential inner decoding. The system uses convolutional codes of very long constraint length and operates on iterations between an inner Fano decoder and an outer Reed-Solomon decoder.

Search methods for rate 1/N convolutional codes used in concatenated systems

Different criteria for selection of good convolutional codes have been tested and the interesting conclusion is that the Viterbi bound on BER evaluated at a higher E/sub b//N/sub 0/ value is the best guide when selecting codes for use on low E/sub b//N/sub 0/ channels.<<ETX>>