An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

Image compression techniques: A survey in lossless and lossy algorithms

novel technique for image retrieval using the color- texture features extracted from images based on vector quantization with Kekre's fast codebook generation is proposed. This gives better discrimination capability for Content Based Image Retrieval (CBIR). Here the database image is divided into 2x2 pixel windows to obtain 12 color descriptors per window (Red, Green and Blue per pixel) to form a vector. Collection of all such vectors is a training set. Then the Kekre's Fast Codebook Generation (KFCG) is applied on this set to get 16 codevectors. The Discrete Cosine Transform (DCT) is applied on these codevectors by converting them to column vector. This transform vector is used as the image signature (feature vector) for image retrieval. The method takes lesser computations as compared to conventional DCT applied on complete image. The method gives the color-texture features of the image database at reduced feature set size. Proposed method avoids resizing of images which is required for any transform based feature extraction method.

Image retrieval using color-texture features from dct on vq code vectors obtained by kekres fast codebook generation

In this paper we present a very simple and yet effective algorithm to generate codebook. The algorithm uses sorting method to generate codebook and the codevectors are obtained by using median approach. The proposed algorithm was experimented on six different images each of size 512 x 512 and four different codebooks of sizes 128, 256, 512 and 1024 are generated. The proposed algorithm is found to be much faster than the LBG and KPE algorithm. The performance of this algorithm is better than LBG and KPE algorithms considering MSE, PSNR and execution time. The proposed algorithm gives less MSE as compared to the LBG for the codebooks of sizes 128, 256, 512 & 1024 respectively. It also gives higher PSNR as compared to LBG for the codebooks of various sizes.

An Efficient Fast Algorithm to Generate Codebook for Vector Quantization

This paper proposes a novel method for fast pattern matching based on dissimilarity functions derived from the Lp norm, such as the Sum of Squared Differences (SSD) and the Sum of Absolute Differences (SAD) The proposed method is full-search equivalent, ie it yields the same results as the Full Search (FS) algorithm In order to pursue computational savings the method deploys a succession of increasingly tighter lower bounds of the adopted Lp norm-based dissimilarity function Such bounding functions allow for establishing a hierarchy of pruning conditions aimed at skipping rapidly those candidates that cannot satisfy the matching criterion The paper includes an experimental comparison between the proposed method and other full-search equivalent approaches known in literature, which proves the remarkable computational efficiency of our proposal

/pdf/full-search-equivalent-pattern-matching-with-incremental-2hxqiq952g.pdf

Full-Search-Equivalent Pattern Matching with Incremental Dissimilarity Approximations

In a framework of vector quantization (VQ), the encoding speed is a key issue for its practical applications. To speed up the VQ encoding process, Walsh transform is introduced in the previous work to map vectors in a k-dimensional (k-D) spatial domain into k-D Walsh domain in order to exploit the energy-compaction property of an orthogonal transform. However, there still exist a serious problem in that previous work because it just simply used the most common sequency-ordered Walsh transform kernel, which is actually not very high efficient for fast VQ encoding. In order to solve the kernel order problem in VQ encoding this paper proposes an optimal order for Walsh transform kernel based on the energy distribution of a particular codebook at each dimension in a k-D Walsh domain, which requires that the dimension with a larger energy distribution be put forward to be as a lower dimension. Experimental results confirmed that the proposed method could reduce the computational cost to 85.9% /spl sim/ 53.1% compared to the previous work so as to enhance its performance obviously.

Enhanced fast encoding method for vector quantization by finding an optimally-ordered Walsh transform kernel

In order to encode an image using VQ fast, it is most important to eliminate unnecessary distance computation as much as possible while searching for winner code. Sum and partial sum are used as features in this paper to roughly measure the difference between an input image block and a code to check whether current code could be a promising candidate winner code or not. A 3-step hierarchical fast search algorithm is proposed by narrowing search scope, skipping redundant distance computation and lastly simplifying must-do distance computation. For 10 standard gray-level images of size 512/spl times/512 with very different details, computational complexity can be reduced to below 5% ultimately for a codebook of size 1024 or 2048 meanwhile keeping the PSNR not as degraded as that of full search.

A hierarchical fast encoding algorithm for vector quantization with PSNR equivalent to full search

Vector quantization (VQ) is a famous asymmetric signal compression method. In VQ, the search process to find the winner for an input vector is extremely time consuming due to a lot of k-dimensional Euclidean distance computations. This property of VQ constrains its practical applications to some extent. In order to speed up the search process of VQ, a unified projection method is proposed in this letter to reject a candidate code vector by a lighter computational burden. This method is universal because it can unify several types of previous works through suitably selecting a projection axis. Furthermore, two criteria for how to select an optimal projection axis for a code vector are proven mathematically, which are most important because they demonstrate the direction for a potential improvement to the search efficiency of VQ. Experimental results of VQ encoding show that the proposed method is very search effective.

A unified projection method for fast search of vector quantization

The L/sub 2/-norm pyramid has already been investigated as a promising data structure for the fast search of vector quantization (VQ) encoding in the previous work. Because the distortion at the top level is always tested first when using such a conventional L/sub 2/-norm pyramid, the top level is most important. In order to enhance the capability of achieving a rejection decision at the top level, a modification is introduced into the conventional L/sub 2/-norm pyramid in this letter by using both the mean and the variance of a vector simultaneously to replace the L/sub 2/-norm of the vector for distortion computation at the top level. Two issues are made clear as 1) why this modification is beneficial to the distortion test is proved and 2) why only the top level of a conventional L/sub 2/-norm pyramid should be modified is interpreted as well. Experimental results confirmed that the performance of VQ encoding by using the modified L/sub 2/-norm pyramid can be improved obviously.

Fast encoding method for vector quantization using modified L/sub 2/-norm pyramid

Vector quantization (VQ) is a well-known signal compression method. In VQ, the search process to find the winner for an input vector either at the codebook generation stage or the VQ encoding stage is extremely time consuming. By using the law of cosines to estimate the Euclidean distance first, Mielikainen has developed a highly efficient full-search-equivalent algorithm. However, some computational redundancies still exist in it. In this letter, we introduce an additional new estimation for the Euclidean distance and then optimize the computing way given by Mielikainen. Mathematical analyses show that our proposed search method can improve Mielikainen's method. And experimental results of VQ encoding demonstrate that the proposed method is very search effective.

Zhibin Pan

Papers

Enhanced fast encoding method for vector quantization by finding an optimally-ordered Walsh transform kernel

A hierarchical fast encoding algorithm for vector quantization with PSNR equivalent to full search

A unified projection method for fast search of vector quantization

Fast encoding method for vector quantization using modified L/sub 2/-norm pyramid

An improved full-search-equivalent vector quantization method using the law of cosines