In this paper, a new edge detector (boundary extractor) is proposed based on finding major change points in a local one-dimensional window of the image intensity values of the rows or columns. The approach amounts to separating the pixels in the window into sets or regions of constant intensities with the edge pixels providing transition points. The edge points are found based on partitioning the interval in an optimal way using dynamic programming with an appropriate cost function. Different cost functions are introduced for the algorithm with simulation results that show the detector's effectiveness even in the presence of noise

A novel technique to classify image edge blocks is presented. It is based on defining a set of linearly independent signature vectors with a one to one association with the edge classes. A set of filter vectors emphasizing the projection of one signature vector and suppressing all others is then designed. Classification of an input edge block is accomplished by choosing the index of the filter with the maximum output magnitude. Coded images based on this classification are shown to preserve their quality and enjoy considerable dB gain over two existing methods. The new technique can be easily implemented using a parallel algorithm with little storage requirement.

The use of space filling curves to encode image input blocks in vector quantization (VQ) is proposed. It is based on computing a group of Peano scannings of selected feature vectors for each codeword in the codebook. An ordered list of the Peano scannings and their link to the codebook is stored. Coding is conducted by restricting the search to two windows of codewords with the closest Peano scannings to that of the input block. Each window center is found in logarithmic time proportional to the codebook size. The number of codewords to be searched is fixed and is determined by some additional distortion that is acceptable over exhaustive search methods. Simulation results produced coded images with no significant degradation while maintaining considerable constant search time savings over exhaustive search methods. The algorithm can be used with some other fast full-search equivalent methods as well.

This letter presents a novel application of Peano scanning (PS) to expedite the partial distance search (PDS) when coding image blocks using vector quantization (VQ). It computes and stores a set of sorted tables of PS values of selected feature vectors associated with the codevectors. A PS acts as a transform from a higher dimension to a single dimension while preserving neighborhood adjacency. Coding of an input block is accomplished by finding the closest PS value to that of the input and its corresponding table. PDS proceeds from the codevector of this PS value. Computational complexity is greatly reduced with the PS value easily obtained in hardware and real time.>