Browsing by Subject "Image compression."

Now showing 1 - 4 of 4

Open Access
A novel compression algorithm based on sparse sampling of 3-D laser range scans
(2010) Dobrucalı, Oğuzcan
3-D models of environments can be very useful and are commonly employed in areas such as robotics, art and architecture, environmental planning and documentation. A 3-D model is typically comprised of a large number of measurements. When 3-D models of environments need to be transmitted or stored, they should be compressed efficiently to use the capacity of the communication channel or the storage medium effectively. In this thesis, we propose a novel compression technique based on compressive sampling, applied to sparse representations of 3-D laser range measurements. The main issue here is finding highly sparse representations of the range measurements, since they do not have such representations in common domains, such as the frequency domain. To solve this problem, we develop a new algorithm to generate sparse innovations between consecutive range measurements acquired while the sensor moves. We compare the sparsity of our innovations with others generated by estimation and filtering. Furthermore, we compare the compression performance of our lossy compression method with widely used lossless and lossy compression techniques. The proposed method offers small compression ratio and provides a reasonable compromise between reconstruction error and processing time.
Open Access
Novel methods for microscopic image processing, analysis, classification and compression
(2013) Suhre, Alexander
Microscopic images are frequently used in medicine and molecular biology. Many interesting image processing problems arise after the initial data acquisition step, since image modalities are manifold. In this thesis, we developed several algorithms in order to handle the critical pipeline of microscopic image storage/ compression and analysis/classification more efficiently. The first step in our processing pipeline is image compression. Microscopic images are large in size (e.g. 100K-by-100K pixels), therefore finding efficient ways of compressing such data is necessary for efficient transmission, storage and evaluation. We propose an image compression scheme that uses the color content of a given image, by applying a block-adaptive color transform. Microscopic images of tissues have a very specific color palette due to the staining process they undergo before data acquisition. The proposed color transform takes advantage of this fact and can be incorporated into widely-used compression algorithms such as JPEG and JPEG 2000 without creating any overhead at the receiver due to its DPCM-like structure. We obtained peak signal-to-noise ratio gains up to 0.5 dB when comparing our method with standard JPEG. The next step in our processing pipeline is image analysis. Microscopic image processing techniques can assist in making grading and diagnosis of images reproducible and by providing useful quantitative measures for computer-aided diagnosis. To this end, we developed several novel techniques for efficient feature extraction and classification of microscopic images. We use region co-difference matrices as inputs for the classifier, which have the main advantage of yielding multiplication-free computationally efficient algorithms. The merit of the co-difference framework for performing some important tasks in signal processing is discussed. We also introduce several methods that estimate underlying probability density functions from data. We use sparsity criteria in the Fourier domain to arrive at efficient estimates. The proposed methods can be used for classification in Bayesian frameworks. We evaluated the performance of our algorithms for two image classification problems: Discriminating between different grades of follicular lymphoma, a medical condition of the lymph system, as well as differentiating several cancer cell lines from each another. Classification accuracies over two large data sets (270 images for follicular lymphoma and 280 images for cancer cell lines) were above 98%.
Open Access
Regularized motion estimation techniques and their applications to video coding
(1996) Kıranyaz, Serkan
Novel regularized motion estimation techniques and their possible applications to video coding are presented. A block matching motion estimation algorithm which extracts better block motion field by forming and ininimizing a suitable energy function is introduced. Based on ciri ¿idciptive structure onto block sizes, cui cidvcinced block matching ¿ilgorithm is presented. The block sizes are adaptively ¿idjusted according to the motion. Blockwise coarse to fine segmentation based motion estimation algorithm is introduced for further reduction on the number of bits that are spent lor the coding of the block motion vectors. Motion estiiricition algorithms which can be used lor ¿iverage motion determination and artificial frame generation by fractional motion compensation are ¿ilso developed. Finallj^, an alternative motion estimation cind compensation technique which defines feciture based motion vectors on the ob ject boundciries and reconstructs the decoded frame from the interpolation of the compensated object boundaries is presented. All the algorithms developed in this thesis are simulated on recil or synthetic images cind their performance is demonstrcited.
Open Access
A very low bit rate video coder decoder
(1997) Bostancı, Hakkı Tunç
A very low bit rate video coding decoding algorithm (codec) that can be used in real-time applications such as videotelephony is proposed. There are established video coding standards (MPEG-1, MPEG-2, H.261, H.263) and standards under development (MPEG-4 and MPEG-7). The proposed cod ing method is based on temporal prediction, followed by spatial prediction and finally entropy coding. The operations are performed in a color-palletized space. A new method based on parameterizing texture features is used for de termining the difference image, which shows the temporal prediction errors. A new hierarchical 2D spatial prediction scheme is proposed for lossy or lossless predictive coding of arbitrary shaped regions. A simulation of the codec is implemented in object oriented C++ language using template based program ming techniques. The codec is tested using official MPEG-4 test sequences. Its performance is compared to H.263 coding at the same bit rate and although numerically inferior, visually similar results are obtained.