511 research outputs found
A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images
Predictive coding is attractive for compression onboard of spacecrafts thanks
to its low computational complexity, modest memory requirements and the ability
to accurately control quality on a pixel-by-pixel basis. Traditionally,
predictive compression focused on the lossless and near-lossless modes of
operation where the maximum error can be bounded but the rate of the compressed
image is variable. Rate control is considered a challenging problem for
predictive encoders due to the dependencies between quantization and prediction
in the feedback loop, and the lack of a signal representation that packs the
signal's energy into few coefficients. In this paper, we show that it is
possible to design a rate control scheme intended for onboard implementation.
In particular, we propose a general framework to select quantizers in each
spatial and spectral region of an image so as to achieve the desired target
rate while minimizing distortion. The rate control algorithm allows to achieve
lossy, near-lossless compression, and any in-between type of compression, e.g.,
lossy compression with a near-lossless constraint. While this framework is
independent of the specific predictor used, in order to show its performance,
in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless
compression standard, obtaining an extension that allows to perform lossless,
near-lossless and lossy compression in a single package. We show that the rate
controller has excellent performance in terms of accuracy in the output rate,
rate-distortion characteristics and is extremely competitive with respect to
state-of-the-art transform coding
Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images
The need for transmission and archive of mammograms and ultrasound Images has
dramatically increased in tele-healthcare applications. Such images require large
amount of' storage space which affect transmission speed. Therefore an effective
compression scheme is essential. Compression of these images. in general. laces a
great challenge to compromise between the higher compression ratio and the relevant
diagnostic information. Out of the many studied compression schemes. lossless
.
IPl. (i-
LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are
chosen based on a comprehensive experimental study carried on a large number of
mammograms and ultrasound images of different sizes and texture. The lossless
schemes are evaluated based on the compression ratio and compression speed. The
distortion in the image quality which is introduced by lossy methods evaluated based
on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio
(PSNR). It is found that lossless compression can achieve a modest compression ratio
2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than
lossless ones but at the price of the image quality which may impede diagnostic
conclusions. In this work, a new compression approach called Ilvbrid Region-based Image
Compression Scheme (IIYRICS) has been proposed for the mammograms and
ultrasound images to achieve higher compression ratios without compromising the
diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode
the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied
on the remaining non region of interest. The results clearly show that this hybrid
strategy can yield high compression ratios with perfect reconstruction of diagnostic
relevant regions, achieving high speed transmission and less storage requirement. For
the sample images considered in our experiment, the compression ratio increases
approximately ten times. However, this increase depends upon the size of the region
of interest chosen. It is also föund that the pre-processing (contrast stretching) of
region of interest improves compression ratios on mammograms but not on ultrasound
images
An efficient error resilience scheme based on wyner-ziv coding for region-of-Interest protection of wavelet based video transmission
In this paper, we propose a bandwidth efficient error resilience scheme for wavelet based video
transmission over wireless channel by introducing an additional Wyner-Ziv (WZ) stream to protect region of
interest (ROI) in a frame. In the proposed architecture, the main video stream is compressed by a generic
wavelet domain coding structure and passed through the error prone channel without any protection.
Meanwhile, the predefined ROI area related wavelet coefficients obtained after an integer wavelet transform
will be specially protected by WZ codec in an additional channel during transmission. At the decoder side, the error-prone ROI related wavelet coefficients will be used as side information to help decoding the WZ stream. Different size of WZ bit streams can be applied in order to meet different bandwidth condition and different
requirement of end users. The simulation results clearly revealed that the proposed scheme has distinct advantages in saving bandwidth comparing with fully applied FEC algorithm to whole video stream and in the meantime offer the robust transmission over error prone channel for certain video applications
Image Compression Techniques: A Survey in Lossless and Lossy algorithms
The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression
Compression of MRI brain images based on automatic extraction of tumor region
In the compression of medical images, region of interest (ROI) based techniques seem to be promising, as they can result in high compression ratios while maintaining the quality of region of diagnostic importance, the ROI, when image is reconstructed. In this article, we propose a set-up for compression of brain magnetic resonance imaging (MRI) images based on automatic extraction of tumor. Our approach is to first separate the tumor, the ROI in our case, from brain image, using support vector machine (SVM) classification and region extraction step. Then, tumor region (ROI) is compressed using Arithmetic coding, a lossless compression technique. The non-tumorous region, non-region of interest (NROI), is compressed using a lossy compression technique formed by a combination of discrete wavelet transform (DWT), set partitioning in hierarchical trees (SPIHT) and arithmetic coding (AC). The classification performance parameters, like, dice coefficient, sensitivity, positive predictive value and accuracy are tabulated. In the case of compression, we report, performance parameters like mean square error and peak signal to noise ratio for a given set of bits per pixel (bpp) values. We found that the compression scheme considered in our setup gives promising results as compared to other schemes
Significant medical image compression techniques: a review
Telemedicine applications allow the patient and doctor to communicate with each other through network services. Several medical image compression techniques have been suggested by researchers in the past years. This review paper offers a comparison of the algorithms and the performance by analysing three factors that influence the choice of compression algorithm, which are image quality, compression ratio, and compression speed. The results of previous research have shown that there is a need for effective algorithms for medical imaging without data loss, which is why the lossless compression process is used to compress medical records. Lossless compression, however, has minimal compression ratio efficiency. The way to get the optimum compression ratio is by segmentation of the image into region of interest (ROI) and non-ROI zones, where the power and time needed can be minimised due to the smaller scale. Recently, several researchers have been attempting to create hybrid compression algorithms by integrating different compression techniques to increase the efficiency of compression algorithms
Hybrid Region-based Image Compression Scheme for Mamograms and Ultrasound Images
The need for transmission and archive of mammograms and ultrasound Images has
dramatically increased in tele-healthcare applications. Such images require large
amount of' storage space which affect transmission speed. Therefore an effective
compression scheme is essential. Compression of these images. in general. laces a
great challenge to compromise between the higher compression ratio and the relevant
diagnostic information. Out of the many studied compression schemes. lossless
.
IPl. (i-
LS and lossy SPII IT are found to he the most efficient ones. JPEG-LS and SI'll IT are
chosen based on a comprehensive experimental study carried on a large number of
mammograms and ultrasound images of different sizes and texture. The lossless
schemes are evaluated based on the compression ratio and compression speed. The
distortion in the image quality which is introduced by lossy methods evaluated based
on objective criteria using Mean Square Error (MSE) and Peak signal to Noise Ratio
(PSNR). It is found that lossless compression can achieve a modest compression ratio
2: 1 - 4: 1. bossy compression schemes can achieve higher compression ratios than
lossless ones but at the price of the image quality which may impede diagnostic
conclusions. In this work, a new compression approach called Ilvbrid Region-based Image
Compression Scheme (IIYRICS) has been proposed for the mammograms and
ultrasound images to achieve higher compression ratios without compromising the
diagnostic quality. In I LYRICS, a modification for JPI; G-LS is introduced to encode
the arbitrary shaped disease affected regions. Then Shape adaptive SPIT IT is applied
on the remaining non region of interest. The results clearly show that this hybrid
strategy can yield high compression ratios with perfect reconstruction of diagnostic
relevant regions, achieving high speed transmission and less storage requirement. For
the sample images considered in our experiment, the compression ratio increases
approximately ten times. However, this increase depends upon the size of the region
of interest chosen. It is also föund that the pre-processing (contrast stretching) of
region of interest improves compression ratios on mammograms but not on ultrasound
images
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