Multiresolution vector quantization

Multiresolution source codes are data compression algorithms yielding embedded source descriptions. The decoder of a multiresolution code can build a source reproduction by decoding the embedded bit stream in part or in whole. All decoding procedures start at the beginning of the binary source description and decode some fraction of that string. Decoding a small portion of the binary string gives a low-resolution reproduction; decoding more yields a higher resolution reproduction; and so on. Multiresolution vector quantizers are block multiresolution source codes. This paper introduces algorithms for designing fixed- and variable-rate multiresolution vector quantizers. Experiments on synthetic data demonstrate performance close to the theoretical performance limit. Experiments on natural images demonstrate performance improvements of up to 8 dB over tree-structured vector quantizers. Some of the lessons learned through multiresolution vector quantizer design lend insight into the design of more sophisticated multiresolution codes

Multi-resolution VQ: parameter meaning and choice

In multi-resolution source coding, a single code is used to give an embedded data description that may be decoded at a variety of rates. Recent work in practical multi-resolution coding treats the optimal design of fixed- and variable-rate tree-structured vector quantizers for multi-resolution coding. In that work the codes are optimized for a designer-specified priority schedule over the system rates, distortions, or slopes. The method relies on a collection of parameters, which may be difficult to choose. This paper explores the meaning and choice of the multi-resolution source coding parameters

Setting priorities: a new SPIHT-compatible algorithm for image compression

We introduce a new algorithm for progressive or multiresolution image compression. The algorithm improves on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm by replacing the SPIHT encoder. The new encoder optimizes the multiresolution code performance relative to a user- defined probability distribution over the code's rates or resolutions. The new algorithm's decoder is identical to the SPIHT decoder. The resulting code achieves the optimal expected performance across resolutions subject to the constraints imposed by the use of the SPIHT decoder and the distribution over resolutions set by the user. The encoder optimization yields performance improvements at the rates or resolutions of greatest importance at the expense of performance degradation at low priority rates or resolutions. The algorithm is fully compatible at the decoder with the original SPIHT algorithm. In particular, the decoder requires no knowledge of the priority function employed at the encoder. Experimental results on an image containing both text and photographic material yield up to 0.86 dB performance improvement over SPIHT at the resolution of highest priority

Setting priorities: a new SPIHT-compatible algorithm for image compression

We introduce a new algorithm for progressive or multiresolution image compression. The algorithm improves on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm by replacing the SPIHT encoder. The new encoder optimizes the multiresolution code performance relative to a user- defined probability distribution over the code's rates or resolutions. The new algorithm's decoder is identical to the SPIHT decoder. The resulting code achieves the optimal expected performance across resolutions subject to the constraints imposed by the use of the SPIHT decoder and the distribution over resolutions set by the user. The encoder optimization yields performance improvements at the rates or resolutions of greatest importance at the expense of performance degradation at low priority rates or resolutions. The algorithm is fully compatible at the decoder with the original SPIHT algorithm. In particular, the decoder requires no knowledge of the priority function employed at the encoder. Experimental results on an image containing both text and photographic material yield up to 0.86 dB performance improvement over SPIHT at the resolution of highest priority

Optimization of Multi-Resolution Source Codes

This thesis studies the optimization of multi-resolution source codes. A multi-resolution source code is a data compression algorithm that generates a bit-stream that can be truncated at any point to reconstruct low-resolution representations of the original data. By progressively refining the description, these codes allow the receiver to get representations of progressively increasing quality from a single file. The optimization methods presented here are based on the minimization of a Lagrangian performance measure, which is a weighted sum of rates and distortions at the different resolutions of the multi-resolution code. The Lagrangian coefficients are the weights that parameterize the priorities assigned to the resolutions. The relative value of these parameters can be set according to the user's preferences regarding which rates are more important, the probability of decoding the file at each possible rate, or any other prioritization rationale. We present a method for converting design constraints into the corresponding Lagrangian parameters. We also use a Lagrangian analysis to investigate optimality properties of multi-resolution codes. Specifically, we explore the characterization of the theoretically optimal output density functions of a two-resolution source code for any arbitrary set of priorities over the resolutions. Once the priority function has been identified, the goal is to design the multi-resolution code that yields the best rate-distortion trade-off for those priorities. The minimization of the multi-resolution Lagrangian is somewhat specific to the framework and type of multi-resolution code. We pursue this goal in several coding frameworks. The first framework is the multi-resolution vector quantizer (MRVQ) framework. Prior work on the topic described optimal MRVQ design for both fixed- and variable-rate systems but implemented only fixed-rate codes. The earliest portion of this thesis began with the implementation of the earlier described algorithm for variable-rate MRVQ for use as a testbed for understanding the important question of how to choose the Lagrangian parameters for multi-resolution codes to meet a collection of desired constraints. Armed with a new understanding of parameter choice in the MRVQ framework, we moved next to the more sophisticated coding framework of wavelet-based embedded bit-plane coders. New results in this framework include improvements on the Set Partitioning in Hierarchical Trees (SPIHT) and the Group Testing for Wavelets (GTW) algorithms that apply the lessons learned from MRVQ theory in these more sophisticated wavelet coding frameworks. Experimental results demonstrate the performance benefits associated with this approach.</p

Teaching Bioeconomics

Bioeconomics is a relatively young field that uses an expanded microeconomics to examine animal behavior, human behavior, and animal and human social institutions. A voluminous literature is rapidly accumulating. There are as yet no standard textbooks, but there are several excellent books and/or articles that can be used in combination with videos and other aids to make a course that students will enjoy and that teachers can use to advance the frontiers of scholarship in economics and biology. Copyright Springer 2005altruism, conflict, cooperation, evolution, game theory, institutions, rationality,