A Low-Complexity Circuit for On-Sensor Concurrent A/D Conversion and Compression

A low-complexity circuit for on-sensor compression is presented. The proposed circuit achieves complexity savings by combining a single-slope analog-to-digital converter with a Golomb-Rice entropy encoder and by implementing a low-complexity adaptation rule. The adaptation rule monitors the output codewords and minimizes their length by incrementing or decrementing the value of the Golomb-Rice coding parameter k. Its hardware implementation is one order of magnitude lower than existing adaptive algorithms. The compression circuit has been fabricated using a 0.35 muM CMOS technology and occupies an area of 0.0918 . Test measurements confirm the validity of the design.     

A constrained joint source/channel coder design

The design of joint source/channel coders in situations where there is residual redundancy at the output of the source coder is examined. It has previously been shown that this residual redundancy can be used to provide error protection without a channel coder. In this paper, this approach is extended to conventional source coder/convolutional coder combinations. A family of nonbinary encoders is developed which more efficiently use the residual redundancy in the source coder output. It is shown through simulation results that the proposed systems outperform conventional source-channel coder pairs with gains of greater than 9 dB in the reconstruction SNR at high probability of error     

Lossless compression of multispectral image data

While spatial correlations are adequately exploited by standard lossless image compression techniques, little success has been attained in exploiting spectral correlations when dealing with multispectral image data. The authors present some new lossless image compression techniques that capture spectral correlations as well as spatial correlation in a simple and elegant manner. The schemes are based on the notion of a prediction tree, which defines a noncausal prediction model for an image. The authors present a backward adaptive technique and a forward adaptive technique. They then give a computationally efficient way of approximating the backward adaptive technique. The approximation gives good results and is extremely easy to compute. Simulation results show that for high spectral resolution images, significant savings can be made by using spectral correlations in addition to spatial correlations. Furthermore, the increase in complexity incurred in order to make these gains is minimal     

Recursively indexed quantization of memoryless sources

A recursively indexed scalar quantizer that performs as well as high-dimensional vector quantizers for several important sources, without the attendant complexity, is presented. The recursively indexed quantizer provides a simple technique, both in terms of design and operation, for use with entropy coding. It also provides a simple quantization technique for use in noisy channel conditions where a variable length code would be inappropriate but performance greater than that provided by Lloyd-Max quantizers is desired     

Joint source/channel coding using arithmetic codes

Reserving space fur a symbol that is not in the source alphabet has been shown to provide excellent error detection. In this paper, we show how to exploit this capability using two sequential decoder structures to provide powerful error correction capability. This joint source/channel coder design provides significant packet loss recovery with minimal rate overhead, and compares favorably with conventional schemes     

An edge preserving differential image coding scheme

Differential encoding techniques are fast and easy to implement. However, a major problem with the use of differential encoding for images is the rapid edge degradation encountered when using such systems. This makes differential encoding techniques of limited utility, especially when coding medical or scientific images, where edge preservation is of utmost importance. A simple, easy to implement differential image coding system with excellent edge preservation properties is presented. The coding system can be used over variable rate channels, which makes it especially attractive for use in the packet network environment.     

Unsupervised learning approach to adaptive differential pulse code modulation

This research is concerned with investigating the problem of data compression utilizing an unsupervised estimation algorithm. This extends previous work utilizing a hybrid source coder which combines an orthogonal transformation with differential pulse code modulation (DPCM). The data compression is achieved in the DPCM loop, and it is the quantizer of this scheme which is approached from an unsupervised learning procedure. The distribution defining the quantizer is represented as a set of separable Laplacian mixture densities for two-dimensional images. The condition of identifiability is shown for the Laplacian case and a decision directed estimate of both the active distribution parameters and the mixing parameters are discussed in view of a Bayesian structure. The decision directed estimators, although not optimum, provide a realizable structure for estimating the parameters which define a distribution which has become active. These parameters are then used to scale the optimum (in the mean square error sense) Laplacian quantizer. The decision criteria is modified to prevent convergence to a single distribution which in effect is the default condition for a variance estimator. This investigation was applied to a test image and the resulting data demonstrate improvement over other techniques using fixed bit assignments and ideal channel conditions.     

Lossless video sequence compression using adaptive prediction.

We present an adaptive lossless video compression algorithm based on predictive coding. The proposed algorithm exploits temporal, spatial, and spectral redundancies in a backward adaptive fashion with extremely low side information. The computational complexity is further reduced by using a caching strategy. We also study the relationship between the operational domain for the coder (wavelet or spatial) and the amount of temporal and spatial redundancy in the sequence being encoded. Experimental results show that the proposed scheme provides significant improvements in compression efficiencies.     

A robust coding scheme for packet video

Some of the important characteristics and requirements of packet video are discussed. A layered packet video coding algorithm based on a progressive transmission scheme is presented. The algorithm provides good compression and can handle significant packet loss with graceful degradation in the reconstruction sequence. A network simulator used in testing the scheme is introduced, and simulation results for various conditions are presented     

An extended least-hop distributed routing algorithm

A routing strategy called NELHNET has been developed for networks with multiprecedence traffic and operating under dynamic traffic and topological conditions. An adaptive distributed algorithm that uses least-hop and least-hop-plus-1 routes in a table of routing vectors, as opposed to the usual table of routing scalars, is described. Current delays are passed backward and forward with the packets to allow development of expected delays to each node via all acceptable routes. The route then selected is the acceptable route with the least expected delay. For speedier recovery, a node returning to service receives the current network status from an adjoining node as soon as the link connecting them is operational. The resultant algorithms show far greater than the marginal improvements originally expected over Arpanet simulations. NELHENET strategies also permit the network to function stably under more heavily loaded conditions than do the Arpanet strategies