Timo Kaukoranta

Aspects of networking in multiplayer computer games

Distributed, real‐time multiplayer computer games (MCGs) are in the vanguard of utilizing the networking possibilities. Although related research has been done in military simulations, virtual reality systems, and computer supported cooperative working, the suggested solutions diverge from the problems posed by MCGs. With this in mind, this paper provides a concise overview of four aspects affecting networking in MCGs. First, networking resources (bandwidth, latency, and computational power) set the technical boundaries within which the MCG must operate. Second, distribution concepts encompass communication architectures (peer‐to‐peer, client/server, server‐network), and both data and control architectures (centralized, distributed, replicated). Third, scalability allows the MCG to adapt to the resource changes by parametrization. Finally, security aims at fighting back against cheating and vandalism, which are common in online gaming.

Compression of Digital Images by Block Truncation Coding: A Survey

Block truncation coding (BTC) is a lossy moment preserving quantization method for compressing digital gray-level images. Its advantages are simplicity, fault tolerance, the relatively high compression efficiency and good image quality of the decoded image. Several improvements of the basic method have been recently proposed in the literature. In this survey we will study the basic algorithm and its improvements by dividing it into three separate tasks; performing quantization, coding the quantization data and cling the bit plane. Each phase of the algorithm will be analyzed separately. On the basis of the analysis, a combined BTC algorithm will be proposed and the comparisons to the standard JPEG algoritbm will be made

Genetic Algorithms for Large-Scale Clustering Problems

We consider the clustering problem in the case where the distances between elements are metric and both the number of attributes and the number of clusters are large. In this environment the genetic algorithm approach gives high quality clusterings, but at the expense of long running time. Three new and efficient crossover techniques are introduced here. The hybridization of the genetic algorithm and k-means algorithm is discussed.

A fast exact GLA based on code vector activity detection

This paper introduces a new method for reducing the number of distance calculations in the generalized Lloyd algorithm (GLA), which is a widely used method to construct a codebook in vector quantization. Reduced comparison search detects the activity of the code vectors and utilizes it on the classification of the training vectors. For training vectors whose current code vector has not been modified, we calculate distances only to the active code vectors. A large proportion of the distance calculations can be omitted without sacrificing the optimality of the partition. The new method is included in several fast GLA variants reducing their running times over 50% on average.

A comparison of lossless compression methods for medical images

In this work, lossless grayscale image compression methods are compared on a medical image database. The database contains 10 different types of images with bit rates varying from 8 to 16 bits per pixel. The total number of test images was about 3000, originating from 125 different patient studies. Methods used for compressing the images include seven methods designed for grayscale images and 18 ordinary general-purpose compression programs. Furthermore, four compressed image file formats were used. The results show that the compression ratios strongly depend on the type of the image. The best methods turned out to be TMW, CALIC and JPEG-LS. The analysis step in TMW is very time-consuming. CALIC gives high compression ratios in a reasonable time, whereas JPEG-LS is nearly as effective and very fast.

Fast and memory efficient implementation of the exact PNN

Straightforward implementation of the exact pairwise nearest neighbor (PNN) algorithm takes O(N3) time, where N is the number of training vectors. This is rather slow in practical situations. Fortunately, much faster implementation can be obtained with rather simple modifications to the basic algorithm. In this paper, we propose a fast O(tauN2) time implementation of the exact PNN, where tau is shown to be significantly smaller than N, We give all necessary data structures and implementation details, and give the time complexity of the algorithm both in the best case and in the worst case. The proposed implementation achieves the results of the exact PNN with the same O(N) memory requirement.

Iterative split-and-merge algorithm for vector quantization codebook generation

We propose a new iterative algorithm for the generation of a codebook in vector quantization. The algorithm starts with an initial code- book that is improved by a combination of merge and split operations. By merging small neighboring clusters, additional resources (codevectors) are released. These extra codevectors can be reallocated by splitting large clusters. This process can be iterated until no further improvement is achieved in the distortion of the codebook. Experimental results show that the proposed method performs well in comparison to other tested methods, including the generalized Lloyd algorithm (GLA) and two hier- archical methods.

On the splitting method for vector quantization codebook generation

The well-known LBG algorithm uses binary splitting for gen- erating an initial codebook, which is then iteratively improved by the generalized Lloyd algorithm (GLA). We study different variants of the splitting method and its application to codebook generation with and without the GLA. A new iterative splitting method is proposed, which is applicable to codebook generation without the GLA. Experiments show that the improved splitting method outperforms both the GLA and the other existing splitting-based algorithms. The best combination uses hy- perplane partitioning of the clusters along the principal axis as proposed by Wu and Zhang, integrated with a local repartitioning phase at each step of the algorithm.

Binary vector quantizer design using soft centroids

Soft centroids method is proposed for binary vector quantizer design. Instead of using binary centroids, the codevectors can take any real value between one and zero during the codebook generation process. The binarization is performed only for the final codebook. The proposed method is successfully applied for three existing codebook generation algorithms: GLA, SA and PNN.

Vector Quantizationby Lazy Pairwise Nearest Neighbor Method

Clustering of a data set can be done by the well-known Pairwise Nearest Neighbor (PNN) algorithm. The algorithm is conceptionally very simple and gives high quality solutions. A drawback of the method is the relatively large running time of the original (exact) implementation. Recently, an efficient version of the exact PNN algorithm has been introduced in literature. In this paper we give a faster implementation of this algorithm. The idea is to postpone the updating of the nearest neighbor information in order to reduce the number of cluster distance calculations. Correctness of the algorithm follows from the monotony of the cluster distances. Practical tests show that the new organization of the algorithm decreases the running time of PNN by ca. 35 per cent.