The present solution gives an optimum algorithm for effective transfer of image codings over a wireless medium. In this paper, the logic behind generating Huffman codes for the gray symbols of an image, its transmission over an AWGN channel and decoding the received code is practically analysed. The coding utilizes a main MATLAB file with associated function to carry out subsequent tasks. The solution carries coherent implementation of predictive, DPCM and Huffman Coding.

The objective of this work is to propose an image denoising technique and compare it with image denoising using ridgelets. The proposed method uses slantlet transform instead of wavelets in ridgelet transform. Experimental result shows that the proposed method is more effective than ridgelets in noise removal. The proposed method is effective in compressing images while preserving edges.

methods

Intelligent coding scheme for wireless image transmission over noisy channels

Image Denoising Techniques Preserving Edges

A vector quantizer is a system for mapping a sequence of continuous or discrete vectors into a digital sequence suitable for communication over or storage in a digital channel. The goal of such a system is data compression: to reduce the bit rate so as to minimize communication channel capacity or digital storage memory requirements while maintaining the necessary fidelity of the data. The mapping for each vector may or may not have memory in the sense of depending on past actions of the coder, just as in well established scalar techniques such as PCM, which has no memory, and predictive quantization, which does. Even though information theory implies that one can always obtain better performance by coding vectors instead of scalars, scalar quantizers have remained by far the most common data compression system because of their simplicity and good performance when the communication rate is sufficiently large. In addition, relatively few design techniques have existed for vector quantizers. During the past few years several design algorithms have been developed for a variety of vector quantizers and the performance of these codes has been studied for speech waveforms, speech linear predictive parameter vectors, images, and several simulated random processes. It is the purpose of this article to survey some of these design techniques and their applications.

Vector quantization

A review of vector quantization techniques used for encoding digital images is presented. First, the concept of vector quantization is introduced, then its application to digital images is explained. Spatial, predictive, transform, hybrid, binary, and subband vector quantizers are reviewed. The emphasis is on the usefulness of the vector quantization when it is combined with conventional image coding techniques, or when it is used in different domains. >

Intelligent coding scheme for wireless image transmission over noisy channels

Citations

Image Denoising Techniques Preserving Edges

References

Vector quantization

Image coding using vector quantization: a review

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