428 research outputs found
Improvements to JPEG-LS via diagonal edge-based prediction
JPEG-LS is the latest pixel based lossless to near lossless still image coding standard introduced by the Joint
Photographic Experts Group (JPEG) '. In this standard simple localized edge detection techniques are used in order to
determine the predictive value of each pixel. These edge detection techniques only detect horizontal and vertical edges
and the corresponding predictors have only been optimized for the accurate prediction of pixels in the locality of
horizontal and/or vertical edges. As a result JPEG-LS produces large prediction enors in the locality of diagonal edges.
In this paper we propose a low complexity, low cost technique that accurately detects diagonal edges and predicts the
value of pixels to be encoded based on the gradients available within the standard predictive template of JPEG-LS. We
provide experimental results to show that the proposed technique outperforms JPEG-LS in terms of predicted mean
squared error, by a margin ofup to 8.5 1%
Exclusive-or preprocessing and dictionary coding of continuous-tone images.
The field of lossless image compression studies the various ways to represent image data in the most compact and efficient manner possible that also allows the image to be reproduced without any loss. One of the most efficient strategies used in lossless compression is to introduce entropy reduction through decorrelation. This study focuses on using the exclusive-or logic operator in a decorrelation filter as the preprocessing phase of lossless image compression of continuous-tone images. The exclusive-or logic operator is simply and reversibly applied to continuous-tone images for the purpose of extracting differences between neighboring pixels. Implementation of the exclusive-or operator also does not introduce data expansion. Traditional as well as innovative prediction methods are included for the creation of inputs for the exclusive-or logic based decorrelation filter. The results of the filter are then encoded by a variation of the Lempel-Ziv-Welch dictionary coder. Dictionary coding is selected for the coding phase of the algorithm because it does not require the storage of code tables or probabilities and because it is lower in complexity than other popular options such as Huffman or Arithmetic coding. The first modification of the Lempel-Ziv-Welch dictionary coder is that image data can be read in a sequence that is linear, 2-dimensional, or an adaptive combination of both. The second modification of the dictionary coder is that the coder can instead include multiple, dynamically chosen dictionaries. Experiments indicate that the exclusive-or operator based decorrelation filter when combined with a modified Lempel-Ziv-Welch dictionary coder provides compression comparable to algorithms that represent the current standard in lossless compression. The proposed algorithm provides compression performance that is below the Context-Based, Adaptive, Lossless Image Compression (CALIC) algorithm by 23%, below the Low Complexity Lossless Compression for Images (LOCO-I) algorithm by 19%, and below the Portable Network Graphics implementation of the Deflate algorithm by 7%, but above the Zip implementation of the Deflate algorithm by 24%. The proposed algorithm uses the exclusive-or operator in the modeling phase and uses modified Lempel-Ziv-Welch dictionary coding in the coding phase to form a low complexity, reversible, and dynamic method of lossless image compression
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
Lossless compression of images with specific characteristics
Doutoramento em Engenharia ElectrotécnicaA compressão de certos tipos de imagens é um desafio para algumas normas de compressão de imagem. Esta tese investiga a compressão sem perdas de imagens com caracterÃsticas especiais, em particular imagens simples, imagens de cor indexada e imagens de microarrays. Estamos interessados no desenvolvimento de métodos de compressão completos e no estudo de técnicas de pré-processamento que possam ser utilizadas em conjunto com as normas de compressão de imagem. A esparsidade do histograma, uma propriedade das imagens simples, é um dos assuntos abordados nesta tese. Desenvolvemos uma técnica de pré-processamento, denominada compactação de histogramas, que explora esta propriedade e que pode ser usada em conjunto com as normas de compressão de imagem para um melhoramento significativo da eficiência de compressão. A compactação de histogramas e os algoritmos de reordenação podem ser usados como préprocessamento para melhorar a compressão sem perdas de imagens de cor indexada. Esta tese apresenta vários algoritmos e um estudo abrangente dos métodos já existentes. Métodos especÃficos, como é o caso da decomposição em árvores binárias, são também estudados e propostos. O uso de microarrays em biologia encontra-se em franca expansão. Devido ao elevado volume de dados gerados por experiência, são necessárias técnicas de compressão sem perdas. Nesta tese, exploramos a utilização de normas de compressão sem perdas e apresentamos novos algoritmos para codificar eficientemente este tipo de imagens, baseados em modelos de contexto finito e codificação aritmética.The compression of some types of images is a challenge for some standard compression techniques. This thesis investigates the lossless compression of images with specific characteristics, namely simple images, color-indexed images and microarray images. We are interested in the development of complete compression methods and in the study of preprocessing algorithms that could be used together with standard compression methods. The histogram sparseness, a property of simple images, is addressed in this thesis. We developed a preprocessing technique, denoted histogram packing, that explores this property and can be used with standard compression methods for improving significantly their efficiency. Histogram packing and palette reordering algorithms can be used as a preprocessing step for improving the lossless compression of color-indexed images. This thesis presents several algorithms and a comprehensive study of the already existing methods. Specific compression methods, such as binary tree decomposition, are also addressed. The use of microarray expression data in state-of-the-art biology has been well established and due to the significant volume of data generated per experiment, efficient lossless compression methods are needed. In this thesis, we explore the use of standard image coding techniques and we present new algorithms to efficiently compress this type of images, based on finite-context modeling and arithmetic coding
A family of stereoscopic image compression algorithms using wavelet transforms
With the standardization of JPEG-2000, wavelet-based image and video
compression technologies are gradually replacing the popular DCT-based methods. In
parallel to this, recent developments in autostereoscopic display technology is now
threatening to revolutionize the way in which consumers are used to enjoying the
traditional 2-D display based electronic media such as television, computer and
movies. However, due to the two-fold bandwidth/storage space requirement of
stereoscopic imaging, an essential requirement of a stereo imaging system is efficient
data compression.
In this thesis, seven wavelet-based stereo image compression algorithms are
proposed, to take advantage of the higher data compaction capability and better
flexibility of wavelets. [Continues.
Contributions to HEVC Prediction for Medical Image Compression
Medical imaging technology and applications are continuously evolving, dealing with images
of increasing spatial and temporal resolutions, which allow easier and more accurate
medical diagnosis. However, this increase in resolution demands a growing amount of
data to be stored and transmitted. Despite the high coding efficiency achieved by the
most recent image and video coding standards in lossy compression, they are not well
suited for quality-critical medical image compression where either near-lossless or lossless
coding is required.
In this dissertation, two different approaches to improve lossless coding of volumetric
medical images, such as Magnetic Resonance and Computed Tomography, were studied
and implemented using the latest standard High Efficiency Video Encoder (HEVC). In a
first approach, the use of geometric transformations to perform inter-slice prediction was
investigated.
For the second approach, a pixel-wise prediction technique, based on Least-Squares prediction,
that exploits inter-slice redundancy was proposed to extend the current HEVC
lossless tools. Experimental results show a bitrate reduction between 45% and 49%, when
compared with DICOM recommended encoders, and 13.7% when compared with standard
HEVC
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