Abstract: In the course of recent decades, medical imaging has
been dominated by the use of costly film media for review and
archival of medical investigation, however due to developments in
networks technologies and common acceptance of a standard digital
imaging and communication in medicine (DICOM) another approach
in light of World Wide Web was produced. Web technologies
successfully used in telemedicine applications, the combination of
web technologies together with DICOM used to design a web-based
and open source DICOM viewer. The Web server allowance to
inquiry and recovery of images and the images viewed/manipulated
inside a Web browser without need for any preinstalling software.
The dynamic site page for medical images visualization and
processing created by using JavaScript and HTML5 advancements.
The XAMPP ‘apache server’ is used to create a local web server for
testing and deployment of the dynamic site. The web-based viewer
connected to multiples devices through local area network (LAN) to
distribute the images inside healthcare facilities. The system offers a
few focal points over ordinary picture archiving and communication
systems (PACS): easy to introduce, maintain and independently
platforms that allow images to display and manipulated efficiently,
the system also user-friendly and easy to integrate with an existing
system that have already been making use of web technologies. The
wavelet-based image compression technique on which 2-D discrete
wavelet transform used to decompose the image then wavelet
coefficients are transmitted by entropy encoding after threshold to
decrease transmission time, stockpiling cost and capacity. The
performance of compression was estimated by using images quality
metrics such as mean square error ‘MSE’, peak signal to noise ratio
‘PSNR’ and compression ratio ‘CR’ that achieved (83.86%) when
‘coif3’ wavelet filter is used.
Abstract: The motivation of image compression technique is to reduce the irrelevance and redundancy of the image data in order to store or pass data in an efficient way from one place to another place. There are several types of compression methods available. Without the help of compression technique, the file size is knowingly larger, usually several megabytes, but by doing the compression technique, it is possible to reduce file size up to 10% as of the original without noticeable loss in quality. Image compression can be lossless or lossy. The compression technique can be applied to images, audio, video and text data. This research work mainly concentrates on methods of encoding, DCT, compression methods, security, etc. Different methodologies and network simulations have been analyzed here. Various methods of compression methodologies and its performance metrics has been investigated and presented in a table manner.
Abstract: Although most digital cameras acquire images in a raw
format, based on a Color Filter Array that arranges RGB color
filters on a square grid of photosensors, most image compression
techniques do not use the raw data; instead, they use the rgb result
of an interpolation algorithm of the raw data. This approach is
inefficient and by performing a lossless compression of the raw data,
followed by pixel interpolation, digital cameras could be more power
efficient and provide images with increased resolution given that the
interpolation step could be shifted to an external processing unit. In
this paper, we conduct a survey on the use of lossless compression
algorithms with raw Bayer images. Moreover, in order to reduce the
effect of the transition between colors that increase the entropy of
the raw Bayer image, we split the image into three new images
corresponding to each channel (red, green and blue) and we study
the same compression algorithms applied to each one individually.
This simple pre-processing stage allows an improvement of more than
15% in predictive based methods.
Abstract: Fractal based digital image compression is a specific
technique in the field of color image. The method is best suited for
irregular shape of image like snow bobs, clouds, flame of fire; tree
leaves images, depending on the fact that parts of an image often
resemble with other parts of the same image. This technique has
drawn much attention in recent years because of very high
compression ratio that can be achieved. Hybrid scheme incorporating
fractal compression and speedup techniques have achieved high
compression ratio compared to pure fractal compression. Fractal
image compression is a lossy compression method in which selfsimilarity
nature of an image is used. This technique provides high
compression ratio, less encoding time and fart decoding process. In
this paper, fractal compression with quad tree and DCT is proposed
to compress the color image. The proposed hybrid schemes require
four phases to compress the color image. First: the image is
segmented and Discrete Cosine Transform is applied to each block of
the segmented image. Second: the block values are scanned in a
zigzag manner to prevent zero co-efficient. Third: the resulting image
is partitioned as fractals by quadtree approach. Fourth: the image is
compressed using Run length encoding technique.
Abstract: Digital images are widely used in computer
applications. To store or transmit the uncompressed images
requires considerable storage capacity and transmission bandwidth.
Image compression is a means to perform transmission or storage of
visual data in the most economical way. This paper explains about
how images can be encoded to be transmitted in a multiplexing
time-frequency domain channel. Multiplexing involves packing
signals together whose representations are compact in the working
domain. In order to optimize transmission resources each 4 × 4
pixel block of the image is transformed by a suitable polynomial
approximation, into a minimal number of coefficients. Less than
4 × 4 coefficients in one block spares a significant amount of
transmitted information, but some information is lost. Different
approximations for image transformation have been evaluated as
polynomial representation (Vandermonde matrix), least squares +
gradient descent, 1-D Chebyshev polynomials, 2-D Chebyshev
polynomials or singular value decomposition (SVD). Results have
been compared in terms of nominal compression rate (NCR),
compression ratio (CR) and peak signal-to-noise ratio (PSNR)
in order to minimize the error function defined as the difference
between the original pixel gray levels and the approximated
polynomial output. Polynomial coefficients have been later encoded
and handled for generating chirps in a target rate of about two
chirps per 4 × 4 pixel block and then submitted to a transmission
multiplexing operation in the time-frequency domain.
Abstract: In this paper a novel color image compression
technique for efficient storage and delivery of data is proposed. The
proposed compression technique started by RGB to YCbCr color
transformation process. Secondly, the canny edge detection method is
used to classify the blocks into the edge and non-edge blocks. Each
color component Y, Cb, and Cr compressed by discrete cosine
transform (DCT) process, quantizing and coding step by step using
adaptive arithmetic coding. Our technique is concerned with the
compression ratio, bits per pixel and peak signal to noise ratio, and
produce better results than JPEG and more recent published schemes
(like CBDCT-CABS and MHC). The provided experimental results
illustrate the proposed technique that is efficient and feasible in terms
of compression ratio, bits per pixel and peak signal to noise ratio.
Abstract: Image compression based on fractal coding is a lossy
compression method and normally used for gray level images range
and domain blocks in rectangular shape. Fractal based digital image
compression technique provide a large compression ratio and in this
paper, it is proposed using YUV colour space and the fractal theory
which is based on iterated transformation. Fractal geometry is mainly
applied in the current study towards colour image compression
coding. These colour images possesses correlations among the colour
components and hence high compression ratio can be achieved by
exploiting all these redundancies. The proposed method utilises the
self-similarity in the colour image as well as the cross-correlations
between them. Experimental results show that the greater
compression ratio can be achieved with large domain blocks but more
trade off in image quality is good to acceptable at less than 1 bit per
pixel.
Abstract: The goal of image segmentation is to cluster pixels
into salient image regions. Segmentation could be used for object
recognition, occlusion boundary estimation within motion or stereo
systems, image compression, image editing, or image database lookup.
In this paper, we present a color image segmentation using
support vector machine (SVM) pixel classification. Firstly, the pixel
level color and texture features of the image are extracted and they
are used as input to the SVM classifier. These features are extracted
using the homogeneity model and Gabor Filter. With the extracted
pixel level features, the SVM Classifier is trained by using FCM
(Fuzzy C-Means).The image segmentation takes the advantage of
both the pixel level information of the image and also the ability of
the SVM Classifier. The Experiments show that the proposed method
has a very good segmentation result and a better efficiency, increases
the quality of the image segmentation compared with the other
segmentation methods proposed in the literature.
Abstract: The Simulation based VLSI Implementation of
FELICS (Fast Efficient Lossless Image Compression System)
Algorithm is proposed to provide the lossless image compression and
is implemented in simulation oriented VLSI (Very Large Scale
Integrated). To analysis the performance of Lossless image
compression and to reduce the image without losing image quality
and then implemented in VLSI based FELICS algorithm. In FELICS
algorithm, which consists of simplified adjusted binary code for
Image compression and these compression image is converted in
pixel and then implemented in VLSI domain. This parameter is used
to achieve high processing speed and minimize the area and power.
The simplified adjusted binary code reduces the number of arithmetic
operation and achieved high processing speed. The color difference
preprocessing is also proposed to improve coding efficiency with
simple arithmetic operation. Although VLSI based FELICS
Algorithm provides effective solution for hardware architecture
design for regular pipelining data flow parallelism with four stages.
With two level parallelisms, consecutive pixels can be classified into
even and odd samples and the individual hardware engine is
dedicated for each one. This method can be further enhanced by
multilevel parallelisms.
Abstract: The Quad Tree Decomposition based performance
analysis of compressed image data communication for lossy and
lossless through wireless sensor network is presented. Images have
considerably higher storage requirement than text. While transmitting
a multimedia content there is chance of the packets being dropped
due to noise and interference. At the receiver end the packets that
carry valuable information might be damaged or lost due to noise,
interference and congestion. In order to avoid the valuable
information from being dropped various retransmission schemes have
been proposed. In this proposed scheme QTD is used. QTD is an
image segmentation method that divides the image into homogeneous
areas. In this proposed scheme involves analysis of parameters such
as compression ratio, peak signal to noise ratio, mean square error,
bits per pixel in compressed image and analysis of difficulties during
data packet communication in Wireless Sensor Networks. By
considering the above, this paper is to use the QTD to improve the
compression ratio as well as visual quality and the algorithm in
MATLAB 7.1 and NS2 Simulator software tool.
Abstract: This paper presents the design, implementation and evaluation of a micro-network, or Network-on-Chip (NoC), based on a generic pipeline router architecture. The router is designed to efficiently support traffic generated by multimedia applications on embedded multi-core systems. It employs a simplest routing mechanism and implements the round-robin scheduling strategy to resolve output port contentions and minimize latency. A virtual channel flow control is applied to avoid the head-of-line blocking problem and enhance performance in the NoC. The hardware design of the router architecture has been implemented at the register transfer level; its functionality is evaluated in the case of the two dimensional Mesh/Torus topology, and performance results are derived from ModelSim simulator and Xilinx ISE 9.2i synthesis tool. An example of a multi-core image processing system utilizing the NoC structure has been implemented and validated to demonstrate the capability of the proposed micro-network architecture. To reduce complexity of the image compression and decompression architecture, the system use image processing algorithm based on classical discrete cosine transform with an efficient zonal processing approach. The experimental results have confirmed that both the proposed image compression scheme and NoC architecture can achieve a reasonable image quality with lower processing time.
Abstract: Steganography is the process of hiding one file inside another such that others can neither identify the meaning of the embedded object, nor even recognize its existence. Current trends favor using digital image files as the cover file to hide another digital file that contains the secret message or information. One of the most common methods of implementation is Least Significant Bit Insertion, in which the least significant bit of every byte is altered to form the bit-string representing the embedded file. Altering the LSB will only cause minor changes in color, and thus is usually not noticeable to the human eye. While this technique works well for 24-bit color image files, steganography has not been as successful when using an 8-bit color image file, due to limitations in color variations and the use of a colormap. This paper presents the results of research investigating the combination of image compression and steganography. The technique developed starts with a 24-bit color bitmap file, then compresses the file by organizing and optimizing an 8-bit colormap. After the process of compression, a text message is hidden in the final, compressed image. Results indicate that the final technique has potential of being useful in the steganographic world.
Abstract: Super-resolution is nowadays used for a high-resolution
image produced from several low-resolution noisy frames. In
this work, we consider the problem of high-quality interpolation of a
single noise-free image. Such images may come from different sources,
i.e., they may be frames of videos, individual pictures, etc. On
the other hand, in the encoder we apply a downsampling via
bidimen-sional interpolation of each frame, and in the decoder we
apply a upsampling by which we restore the original size of the
image. If the compression ratio is very high, then we use a
convolutive mask that restores the edges, eliminating the blur.
Finally, both, the encoder and the complete decoder are implemented
on General-Purpose computation on Graphics Processing Units
(GPGPU) cards. In fact, the mentioned mask is coded inside texture
memory of a GPGPU.
Abstract: An image compression method has been developed
using fuzzy edge image utilizing the basic Block Truncation Coding
(BTC) algorithm. The fuzzy edge image has been validated with
classical edge detectors on the basis of the results of the well-known
Canny edge detector prior to applying to the proposed method. The
bit plane generated by the conventional BTC method is replaced with
the fuzzy bit plane generated by the logical OR operation between
the fuzzy edge image and the corresponding conventional BTC bit
plane. The input image is encoded with the block mean and standard
deviation and the fuzzy bit plane. The proposed method has been
tested with test images of 8 bits/pixel and size 512×512 and found to
be superior with better Peak Signal to Noise Ratio (PSNR) when
compared to the conventional BTC, and adaptive bit plane selection
BTC (ABTC) methods. The raggedness and jagged appearance, and
the ringing artifacts at sharp edges are greatly reduced in
reconstructed images by the proposed method with the fuzzy bit
plane.
Abstract: Image compression can improve the performance of
the digital systems by reducing time and cost in image storage
and transmission without significant reduction of the image quality.
Furthermore, the discrete cosine transform has emerged as the new
state-of-the art standard for image compression. In this paper, a
hybrid image compression technique based on reversible blockade
transform coding is proposed. The technique, implemented over
regions of interest (ROIs), is based on selection of the coefficients
that belong to different transforms, depending on the coefficients is
proposed. This method allows: (1) codification of multiple kernals
at various degrees of interest, (2) arbitrary shaped spectrum,and (3)
flexible adjustment of the compression quality of the image and the
background. No standard modification for JPEG2000 decoder was
required. The method was applied over different types of images.
Results show a better performance for the selected regions, when
image coding methods were employed for the whole set of images.
We believe that this method is an excellent tool for future image
compression research, mainly on images where image coding can
be of interest, such as the medical imaging modalities and several
multimedia applications. Finally VLSI implementation of proposed
method is shown. It is also shown that the kernal of Hartley and
Cosine transform gives the better performance than any other model.
Abstract: Image compression plays a vital role in today-s
communication. The limitation in allocated bandwidth leads to
slower communication. To exchange the rate of transmission in the
limited bandwidth the Image data must be compressed before
transmission. Basically there are two types of compressions, 1)
LOSSY compression and 2) LOSSLESS compression. Lossy
compression though gives more compression compared to lossless
compression; the accuracy in retrievation is less in case of lossy
compression as compared to lossless compression. JPEG, JPEG2000
image compression system follows huffman coding for image
compression. JPEG 2000 coding system use wavelet transform,
which decompose the image into different levels, where the
coefficient in each sub band are uncorrelated from coefficient of
other sub bands. Embedded Zero tree wavelet (EZW) coding exploits
the multi-resolution properties of the wavelet transform to give a
computationally simple algorithm with better performance compared
to existing wavelet transforms. For further improvement of
compression applications other coding methods were recently been
suggested. An ANN base approach is one such method. Artificial
Neural Network has been applied to many problems in image
processing and has demonstrated their superiority over classical
methods when dealing with noisy or incomplete data for image
compression applications. The performance analysis of different
images is proposed with an analysis of EZW coding system with
Error Backpropagation algorithm. The implementation and analysis
shows approximately 30% more accuracy in retrieved image
compare to the existing EZW coding system.
Abstract: Discrete Cosine Transform (DCT) based transform coding is very popular in image, video and speech compression due to its good energy compaction and decorrelating properties. However, at low bit rates, the reconstructed images generally suffer from visually annoying blocking artifacts as a result of coarse quantization. Lapped transform was proposed as an alternative to the DCT with reduced blocking artifacts and increased coding gain. Lapped transforms are popular for their good performance, robustness against oversmoothing and availability of fast implementation algorithms. However, there is no proper study reported in the literature regarding the statistical distributions of block Lapped Orthogonal Transform (LOT) and Lapped Biorthogonal Transform (LBT) coefficients. This study performs two goodness-of-fit tests, the Kolmogorov-Smirnov (KS) test and the 2- test, to determine the distribution that best fits the LOT and LBT coefficients. The experimental results show that the distribution of a majority of the significant AC coefficients can be modeled by the Generalized Gaussian distribution. The knowledge of the statistical distribution of transform coefficients greatly helps in the design of optimal quantizers that may lead to minimum distortion and hence achieve optimal coding efficiency.
Abstract: In the framework of the image compression by
Wavelet Transforms, we propose a perceptual method by
incorporating Human Visual System (HVS) characteristics in the
quantization stage. Indeed, human eyes haven-t an equal sensitivity
across the frequency bandwidth. Therefore, the clarity of the
reconstructed images can be improved by weighting the quantization
according to the Contrast Sensitivity Function (CSF). The visual
artifact at low bit rate is minimized. To evaluate our method, we use
the Peak Signal to Noise Ratio (PSNR) and a new evaluating criteria
witch takes into account visual criteria. The experimental results
illustrate that our technique shows improvement on image quality at
the same compression ratio.
Abstract: In this study, a novel approach of image embedding is introduced. The proposed method consists of three main steps. First, the edge of the image is detected using Sobel mask filters. Second, the least significant bit LSB of each pixel is used. Finally, a gray level connectivity is applied using a fuzzy approach and the ASCII code is used for information hiding. The prior bit of the LSB represents the edged image after gray level connectivity, and the remaining six bits represent the original image with very little difference in contrast. The proposed method embeds three images in one image and includes, as a special case of data embedding, information hiding, identifying and authenticating text embedded within the digital images. Image embedding method is considered to be one of the good compression methods, in terms of reserving memory space. Moreover, information hiding within digital image can be used for security information transfer. The creation and extraction of three embedded images, and hiding text information is discussed and illustrated, in the following sections.
Abstract: In this work, we present a comparison between
different techniques of image compression. First, the image is
divided in blocks which are organized according to a certain scan.
Later, several compression techniques are applied, combined or
alone. Such techniques are: wavelets (Haar's basis), Karhunen-Loève
Transform, etc. Simulations show that the combined versions are the
best, with minor Mean Squared Error (MSE), and higher Peak Signal
to Noise Ratio (PSNR) and better image quality, even in the presence
of noise.