Adjustable compression method for still JPEG images

There are a large number of image processing applications that work with different performance requirements and available resources. Recent advances in image compression focus on reducing image size and processing time, but offer no real-time solutions for providing time/quality flexibility of the resulting image, such as using them to transmit the image contents of web pages. In this paper we propose a method for encoding still images based on the JPEG standard that allows the compression/decompression time cost and image quality to be adjusted to the needs of each application and to the bandwidth conditions of the network. The real-time control is based on a collection of adjustable parameters relating both to aspects of implementation and to the hardware with which the algorithm is processed. The proposed encoding system is evaluated in terms of compression ratio, processing delay and quality of the compressed image when compared with the standard method

Learning Algorithm effect on Multilayer Feed Forward Artificial Neural Network performance in image coding

One of the essential factors that affect the performance of Artificial Neural Networks is the learning algorithm. The performance of Multilayer Feed Forward Artificial Neural Network performance in image compression using different learning algorithms is examined in this paper. Based on Gradient Descent, Conjugate Gradient, Quasi-Newton techniques three different error back propagation algorithms have been developed for use in training two types of neural networks, a single hidden layer network and three hidden layers network. The essence of this study is to investigate the most efficient and effective training methods for use in image compression and its subsequent applications. The obtained results show that the Quasi-Newton based algorithm has better performance as compared to the other two algorithms

IWCD-PROPOSED IMAGE COMPRESSION METHOD BASED ON INTEGER WAVELET AND DCT

This paper describes a new lossy image compression decompression algorithm. In lossy compression techniques there are some loss of information, and image cannot be reconstructed exactly.This algorithm will be referred to as (IWDC), which stands for integer wavelet (IWT) and discrete cosine transform (DCT) and this algorithm improves existing techniques and develops new image compressors.(IWDC) is efficient than corresponding DCT and wavelet transform functions and incorporating DCT and integer wavelet transform are shown to improve the performance of the DCT and integer wavelet (IWT). In the new proposed compression is more efficient than the still image compression methods

Effects of JPEG and JPEG2000 lossy compression on remote sensing image classification for mapping crops and forest areas

This study measures the effect of lossy image compression on the digital classification of crops and forest areas. A hybrid classification method using satellite images and other variables has been used. The results contribute interesting new data on the influence of compression on the quality of the produced cartography, both from a "by pixel" perspective and regarding the homogeneity of the obtained polygons. The classified area in classifications only carried out with radiometric variables or with NDVI and humidity (for crops) increases as image compression increases, although the increase is smaller for JPEG2000 formats and for crops. On the other hand, the classified area decreases in classifications which also take into account topoclimatic variables (for forests). Overall image accuracy diminishes at high compression ratios (CR), although the point of inflection occurs at different CR depending on the compression format. As a rule, the JPEG2000 format gives better results quantitatively for forests (accuracy and classified area) and visually (images with less "salt and pepper" effect) for both land covers

A Low Power Application-Specific Integrated Circuit (ASIC) Implementation of Wavelet Transform/Inverse Transform

A unique ASIC was designed implementing the Haar Wavelet transform for image compression/decompression. ASIC operations include performing the Haar wavelet transform on a 512 by 512 square pixel image, preparing the image for transmission by quantizing and thresholding the transformed data, and performing the inverse Haar wavelet transform, returning the original image with only minor degradation. The ASIC is based on an existing four-chip FPGA implementation. Implementing the design using a dedicated ASIC enhances the speed, decreases chip count to a single die, and uses significantly less power compared to the FPGA implementation. A reduction of RAM accesses was realized and a tradeoff between states and duplication of components for parallel operation were key to the performance gains. Almost half of the external RAM accesses were removed from the FPGA design by incorporating an internal register file. This reduction reduced the number of states needed to process an image increasing the image frame rate by 13% and decreased I/O traffic on the bus by 47%. Adding control lines to the ALU components, thus eliminating unnecessary switching of combination logic blocks, further reduced power requirements. The 22 mm2 ASIC consumes an estimated 430 mW of power when operating at the maximum frequency of 17 MHz