Survey on Swarm Intelligence Based Optimization Technique for Image Compression

نویسندگان

  • Vivek G
  • Vrinda Shetty
چکیده

Image compression is one of the most important and successful applications of the wavelet transform. Images will have huge amount of information which require more storage space, and high transmission bandwidths and more transmission time. So it is important to compress the image by eliminating the redundant information in the image and encoding only the essential information. As multimedia information‟s are growing in huge numbers there is a need for compression, to reduce the hardware storage space and bandwidths for transmission. Hence image compression has become a necessity. This paper reviews some of the optimization techniques used for image compression. These techniques are based on Swarm intelligence. Swarm Intelligence is based on the collective behaviour of self-organized, decentralized, artificial or natural systems. This paper reviews some of the optimization algorithms based on Swarm intelligence like Ant colony optimization, Artificial Bee Colony Algorithm, Particle swarm optimization, and the Intelligent water drop algorithm.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Implementation of VlSI Based Image Compression Approach on Reconfigurable Computing System - A Survey

Image data require huge amounts of disk space and large bandwidths for transmission. Hence, imagecompression is necessary to reduce the amount of data required to represent a digital image. Thereforean efficient technique for image compression is highly pushed to demand. Although, lots of compressiontechniques are available, but the technique which is faster, memory efficient and simple, surely...

متن کامل

Adaptive fractal image compression using PSO

At low bitrate and with acceptable quality in Fractal Image Compression (FIC) of the coded image, the encoding time is very large for most existing algorithms. In this paper, a fast fractal encoding system is proposed using particle swarm optimization (PSO) to reduce the encoding time. Here, an optimization technique is used for the MSE based on the stopping criterion between range block and do...

متن کامل

Integrated Particle Swarm Optimization and Genetic Algorithm Based Compression for Reduction of Blocking Artifacts

Image compression has become very important tool in digital image processing. The main objective of the compression is to reduce the amount or unwanted data while retaining the information in the image. The goal behind is to save the amount of memory required to save the image(s) or to utilize network bandwidth in efficient manner. Transformbased compression is extensively used for image compre...

متن کامل

A Hybrid Computational Intelligence Algorithm for Automatic Skin Lesion Segmentation in Dermoscopy Images

In this paper, an unsupervised approach based on Evolving Vector Quantization (EVQ) is presented for enhancing dermatology images for skin lesion segmentation. Vector Quantization (VQ) as a famous compression technique has been widely used in image signal compression and speech signal compression. The EVQ algorithm extends the Linde, Buzo, and Gray (LBG) Vector Quantization method with Particle...

متن کامل

Color Image Quantization based on Bacteria Foraging Optimization

Bacterial Foraging Optimization (BFO) is optimization technique proposed by K. M. Passino in 2002 To tackle complex search problems of the real world, scientists have been drawing inspiration from nature and natural creatures for years. Bacterial Foraging Optimization is a burgeoning nature inspired technique to find the optimal solution of the problem. A Color images Quantization is necessary ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015