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In this paper, the author uses Chomsky's parse trees to compare Sanskrit and Huffman coding as two different compression schemes. The paper begins with a review of parse trees in English and Sanskrit and goes on to propose the aforesaid comparison.

Traditionally, images are understood based on their primitive features such as color, texture, and shape. The proposed feature extraction methods usually cover a range of primitive features. SIFT, for example, in addition to the shape-based information, extracts texture and color information to some extent. Thus, different descriptors may cover a common range of primitive features which we call information overlap. Selecting a set of feature descriptors with low information overlap allows more comprehensive understanding of the data by providing a broader range of new features. This article introduces a new method based on information theory for comparing various descriptors. The idea is to code each description of an image by Huffman coding. The distance between the coded descriptions are then measured using Levenshtein distance as the information overlap. Results show that the computed information overlap clearly describes the differences between the learning from different descriptions of Earth Observation images.

Some Huffman codes contain a special codeword called a synchronizing codeword, which resynchronizes the code whenever it is transmitted. We demonstrate properties of synchronizing codewords and, in particular, give some relationships between the length of the shortest codeword and the length and structure of the synchronizing codeword. A tight upper bound and some lower bounds are presented. We show that given a shortest codeword of length m and a synchronizing codeword of length r m − 1 then the code also contains other synchronizing codewords. We calculate the number and lengths of these codewords. Finally, several examples of good codes are given.

This paper deals with the design of coded-excitation signal for medical ultrasound imaging. In order to design a code sequence that generates an ultrasound signal with good detail resolution and signal-to-noise ratio, both in frequency-dependent and frequency-independent attenuating media, we propose to use a linear Huffman code obtained by an efficiency-driven optimizing procedure. By resorting to computer simulations, we show that this approach is particularly effective and it outperforms other linear coding schemes commonly used in coded-excitation ultrasound imaging.

Date of Conference: 8-11 October 2006 Conference Name: International Conference on Image Processing, IEEE 2006 In this paper, images in Color Filter Array (CFA) format are compressed without converting them to full-RGB color images. Green pixels are extracted from the CFA image data and placed in a rectangular array, and compressed using a transform based method without estimating the corresponding luminance values. In addition, two sets of color difference (or chrominance) coefficients are obtained corresponding to the red and blue pixels of the CFA data and they are also compressed using a transform based method. The proposed method produces better PSNR values compared to the standard approach of bilinear interpolation followed by compression.

Describes a near-linear-time algorithm for a variant of Huffman coding, in which the letters may have non-uniform lengths (as in Morse code), but with the restriction that each word to be encoded has equal probability. [See also ``Huffman Coding with Unequal Letter Costs'' (2002).], Comment: proceedings version in ICALP (1994)