Nur Azman Abu

An efficient compact Tchebichef Moment for image compression

Orthogonal moment functions have long been used in image analysis. This paper proposes a novel approach based on discrete orthogonal Tchebichef Moment for efficient image compression. The method incorporates simplified mathematical framework techniques using matrices, as well as a block-wise reconstruction technique to eliminate possible occurrences of numerical instabilities at higher moment orders. The comparison between Tchebichef Moment compression and JPEG compression has been done. The results show significant advantages for Tchebichef Moment in terms of its image quality and compression rate. Tchebichef moment provides a more compact support to the image via sub-block reconstruction for compression. Tchebichef Moment Compression has clear potential to perform better for broader domain on real digital images and graphically generated images.

Using tchebichef moment for fast and efficient image compression

Orthogonal moment is known as better moment functions compared to the non-orthogonal moment. Among all the orthogonal moments, Tchebichef Moment appear to be the most recent moment functions that still attract the interest among the computer vision researchers. This paper proposes a novel approach based on discrete orthogonal Tchebichef Moment for an efficient image compression. The image compression is useful in many applications especially related to images that are needed to be seen in small devices such as in mobile phone. Meanwhile, the method incorporates simplified mathematical framework techniques using matrices, as well as a block-wise reconstruction technique to eliminate possible occurrences of numerical instabilities at higher moment orders. In addition, a comparison between Tchebichef Moment compression and JPEG compression is conducted. The result shows significant advantages for Tchebichef Moment in terms of its image quality and compression rate. Tchebichef moment provides a more compact support to the image via sub-block reconstruction for compression. Tchebichef Moment Compression is able to perform potentially better for a broader domain on real digital images and graphically generated images.

A generic psychovisual error threshold for the quantization table generation on JPEG image compression

The quantization process is a main part of image compression to control visual quality and the bit rate of the image output. The JPEG quantization tables are obtained from a series of psychovisual experiments to determine a visual threshold. The visual threshold is useful in handling the intensity level of the colour image that can be perceived visually by the human visual system. This paper will investigate a psychovisual error threshold at DCT frequency on the grayscale image. The DCT coefficients are incremented one by one for each frequency order. Whereby, the contribution of DCT coefficients to the error reconstruction will be a primitive pyschovisual error. At certain threshold being set on this psychovisual error, the new quantization table can be generated. The experimental results show that the new quantization table from psychovisual error threshold for DCT basis functions gives better quality image at lower average bit length of Huffman code than standard JPEG image compression.

Fast 4x4 Tchebichef Moment Image Compression

This paper proposes a new approach based on 4x4 discrete orthogonal Tchebichef Moment for fast and efficient image compression. The method incorporates a simplified mathematical framework technique using matrices, as well as a block-wise reconstruction technique to eliminate possible occurrences of numerical instabilities at higher moment orders. The comparison between 4x4 Tchebichef Moment Transform and Discrete Cosine Transform has been done. The results show significant advantages for 4x4 Tchebichef Moment in terms of its error reconstruction and average bit-length of Huffman codes.

An efficient 2×2 Tchebichef moments for mobile image compression

Currently mobile digital image applications transmit a lot of images back and forth. Image compression is needed to reduce transmission payload at the expense of lower quality. At the same time, mobile devices are only expected to be equipped with lower computing power and storage. They need an efficient compression scheme especially for small images. The standard JPEG using discrete Cosine transform is a popular lossy image compression. Alternatively, this paper introduces 2×2 Tchebichef moments transform for the efficient image compression. In the previous research, larger sub-block discrete Tchebichef moments have been used extensively for image compression. The comparisons between JPEG compression and 2×2 Tchebichef moments image compressions shall be done. The preliminary experiment results show that 2×2 Tchebichef moments transform has the potential to easily perform better than JPEG image compression. The 2×2 Tchebichef moments provides an efficient and compact support for image compression.

Image Super-Resolution via Discrete Tchebichef Moment

Super-resolution is a set of methods of increasing or doubling image resolution. Super-resolution algorithms can be divided into frequency or space domain. In this paper, a simple technique shall be applied in both space and frequency domain of an image. This transform integrates a simplified mathematical framework technique using matrices, as well as a block-wise reconstruction technique. Tchebichef moment has been chosen here since it performs better than the popular Discrete Cosine Transform.

ADAPTIVE TCHEBICHEF MOMENT TRANSFORM IMAGE COMPRESSION USING PSYCHOVISUAL MODEL

An extension of the standard JPEG image compression known as JPEG-3 allows rescaling of the quantization matrix to achieve a certain image outp ut quality. Recently, Tchebichef Moment Transform (TMT) has been introduced in the field of image compression. TMT has been shown to perform better than the standard JPEG image compression. This study presents an adaptive TMT image compression. This task is obtained by generating custom quantization tables f or low, medium and high image output quality levels based on a psychovisual model. A psychovisual model is developed to approximate visual threshold on Tchebichef moment from image reconstruction error. The contribution of each moment will be investigated and analy zed in a quantitative experiment. The sensitivity of TM T basis functions can be measured by evaluating the ir contributions to image reconstruction for each mome nt order. The psychovisual threshold model allows a developer to design several custom TMT quantization tables for a user to choose from according to his or her target output preference. Consequently, these quant ization tables produce lower average bit length of Huffman code while still retaining higher image quality tha n the extended JPEG scaling scheme.

Image watermarking using psychovisual threshold over the edge

Currently the digital multimedia data can easily be copied. Digital image watermarking is an alternative approach to authentication and copyright protection of digital image content. An alternative embedding watermark based on human eye properties can be used to effectively hide the watermark image. This paper introduces the embedding watermark scheme along the edge based on the concept of psychovisual threshold. This paper will investigate the sensitivity of minor changes in DCT coefficients against JPEG quantization tables. Based on the concept of psychovisual threshold, there are still deep holes in JPEG quantization values to embed a watermark. This paper locates and utilizes them to embed a watermark. The proposed scheme has been tested against various non-malicious attacks. The experiment results show the watermark is robust against JPEG image compression, noise attacks and low pass filtering.

Tchebichef moment transform on image dithering for mobile applications

Currently, mobile image applications spend a lot of computing process to display images. A true color raw image contains billions of colors and it consumes high computational power in most mobile image applications. At the same time, mobile devices are only expected to be equipped with lower computing process and minimum storage space. Image dithering is a popular technique to reduce the numbers of bit per pixel at the expense of lower quality image displays. This paper proposes a novel approach on image dithering using 2x2 Tchebichef moment transform (TMT). TMT integrates a simple mathematical framework technique using matrices. TMT coefficients consist of real rational numbers. An image dithering based on TMT has the potential to provide better efficiency and simplicity. The preliminary experiment shows a promising result in term of error reconstructions and image visual textures.

Spectrum Analysis of Speech Recognition via Discrete Tchebichef Transform

Speech recognition is still a growing field. It carries strong potential in the near future as computing power grows. Spectrum analysis is an elementary operation in speech recognition. Fast Fourier Transform (FFT) is the traditional technique to analyze frequency spectrum of the signal in speech recognition. Speech recognition operation requires heavy computation due to large samples per window. In addition, FFT consists of complex field computing. This paper proposes an approach based on discrete orthonormal Tchebichef polynomials to analyze a vowel and a consonant in spectral frequency for speech recognition. The Discrete Tchebichef Transform (DTT) is used instead of popular FFT. The preliminary experimental results show that DTT has the potential to be a simpler and faster transformation for speech recognition.