Light fields can nowadays be acquired by several methods and devices in the form of light field images, which are at the core of new forms of media technologies. Many research challenges are still open in light field imaging, such as data representation formats, data compression tools, communication protocols, subjective and objective quality of experience measurement metrics and methods. This paper presents a brief overview of the current architecture of the JPEG Pleno light field coding standard under development within the JPEG committee (ISO/IEC JTC1/SC29/WG1). Thereafter, a comparative analysis between the performance of the JPEG Pleno Light Field codec under various modes and configurations and the performance of the considered anchor codecs is reported and discussed.
JPEG Pleno provides a standard framework to facilitate the capture, representation, and exchange of light field, point cloud and holographic imaging modalities. JPEG Pleno Part 2 addresses coding of light field data. Two coding modes are supported for this modality. The first mode exploits the redundancy in this 4D data by utilizing a 4D transform technique, the second mode is based on 4D prediction. Both techniques are outlined in this paper as well as the file format that encapsulates the resulting codestreams.
In recent years, we have observed the advent of plenoptic modalities such as light fields, point clouds and holography in many devices and applications. Besides plenty of technical challenges brought by these new modalities, a particular challenge is arising at the horizon, namely providing interoperability between these devices and applications, and – in addition – at a cross-modality level. Based on these observations the JPEG committee (ISO/IEC JTC1/SC29/WG1 and ITU-T SG16) has initiated a new standardization initiative – JPEG Pleno – that is intended to define an efficient framework addressing the above interoperability issues. In this paper, an overview is provided about its current status and future plans.
Kolmogorov's structure function (KSF) is used in the algorithmic theory of complexity for describing the structure of a string by use of models (programs) of increasing complexity. Recently, inspired by the structure function, an extension of the minimum description length theory was introduced for achieving a decomposition of the
total description of the data into a noise part and a model part, where the models are parametric distributions instead of programs, the code length necessary for the model part being restricted by a parameter. In this way a new "rate-distortion" type of curve is obtained, which may be further used as a general model of the data,
quantifying the amount of noise left "unexplained" by models of increasing complexity. In this paper we present a complexity-noise function for a class of hierarchical image models in the wavelet
transform domain, in the spirit of the Kolmogorov structure function. The minimization of the model description can be shown to have a form similar to one resulting from the minimization in the rate-distortion sense, and thus it will be achieved as in lossy image compression. As an application of the complexity-noise function introduced we study the image denoising problem and analyze the conditions under which the best reconstruction along the complexity-noise function is obtained.
Molecular classification of tumors holds great potential for cancer research, diagnosis, and treatment. In this study, we apply a novel classification technique to cDNA microarray data for discriminating between three subtypes of malignant lymphoma: CD5+ diffuse large B-cell lymphoma, CD5- diffuse large B-cell lymphoma, and mantle cell lymphoma. The proposed technique combines the k-Nearest Neighbor (k-NN) algorithm with optimized data quantization. The feature genes on which the classification is based are selected by ranking them according to their separability criteria computed by taking into account between-class and within-class scatter. The classification errors, estimated using cross-validation, are significantly lower than those produced by classical variants of the k-NN algorithm. Multidimensional scaling and hierarchical clustering dendrograms are used to visualize the separation of the three subtypes of lymphoma.
Lossless image compression has become an important research topic,
especially in relation with the JPEG-LS standard. Recently, the techniques known for designing optimal codes for sources with infinite alphabets have been applied for the quantized Laplacian sources which have probability mass functions with two geometrically decaying tails. Due to the simple parametric model of the source distribution the Huffman iterations are possible to be carried out analytically, using the concept of reduced source, and the final codes are obtained as a sequence of very simple arithmetic operations, avoiding the need to store coding tables. We propose the use of these (optimal) codes in conjunction with context-based
prediction, for noiseless compression of images. To reduce further the average
code length, we design Escape sequences to be employed when the estimation
of the distribution parameter is unreliable.
Results on standard test files show improvements in compression ratio when comparing with JPEG-LS.
KEYWORDS: Distortion, Quantization, Image compression, Wavelets, Computer programming, Wavelet transforms, Telecommunications, Signal to noise ratio, Data compression, Data communications
This paper presents an image coding method, based on wavelet
transform, where the distribution of the subband coefficients is
assumed to be generalized Gaussian. The shape factor and the standard
deviation are estimated in all subbands. A procedure of bit allocation
distributes then the available bitrate to the retained coefficients.
The multiple scale leader lattice vector quantization (MSLLVQ) has
shown its superiority compared to other structured quantization
schemes and now we propose its use for the quantization of the wavelet
coefficients. The main contribution of the paper is the procedure for
selecting the structure and the leaders for the MSLLVQ. An iterative
construction of the MSLLVQ scheme is presented along with the
derivation of the operational rate-distortion function. The
bit allocation procedure is based on the exponential fitting of the
operational rate-distortion curve. The results in terms of
peak signal to noise ratio are compared to other image codecs from the
literature, the advantage of such a coding structure being
particularly important for the fixed rate encoding.
This paper compares several discrimination methods for the classification of tumors using gene expression data. We introduce variations of known classification methods, and compare the effects of quantizing the data prior to applying various methods, and also discuss the selection of the distance function. The error rates obtained with the new methods are shown to be smaller than those reported in recently published studies.
This paper develops new algorithms belonging to the class of context modeling methods, with direct application to lossless coding of gray level images. The prediction stage and the context modeling stage are performed using nonlinear techniques rooted in the field of order statistics nonlinear filtering, which proved competitive in image restoration applications. The new nonlinear predictors introduced here can be easily rephrased as adaptive nonlinear filtering tools, useful in image restoration applications. We propose a new variant of the Context algorithm, where the prediction, modeling of errors and coding are realized using a Finite State Machine modeler, (which reduces the complexity of tree modelers, by lumping together similar nodes). The coding performance of the new Context algorithm is better than that of the best available algorithms, as illustrated in the experimental section.
Given a set of cost coefficients, obtained from a "representative" training data set and some desired set, we have
previously shown that the optimal Boolean filter, based on a defined error criterion, is obtained by simple compare/assign
operations. If, on the other hand, the desired solution is a stack filter, three steps must be added to the above procedure.
Following the compare/assign step above, we check if the resulting solution is of the desired type. If not, we compute
the maximal positive Boolean function contained in the resulting Boolean function. Finally, we check if adding other
minterms to the positive Boolean function obtained in the previous step will improve the criterion value.
The first step requires a very low computational effort. For the following three steps, matrix based procedures using
the stacking matrix, are derived. First, we derive a fast procedure for checking the positivity of a Boolean function.
This procedure can be written in a single line using Matlab® language. The following step consists of finding the
maximal positive Boolean function embedded in a given Boolean function. Again, a fast procedure is derived for this
task, which can also be written in one line using Matlab® language. The final step checks for improvement, in the cost
criterion, when adding other minterms to the positive Boolean function, resulting from the previous step. We will use
again the stacking matrix to accomplish this task, resulting in a three-line Matlab®code. Some examples are provided
to illustrate each step in the above procedure.
This paper analyzes the properties of some layered structures formed by cascading layers of Boolean and stack filters and solves the optimal design problem using techniques developed under a training framework. We propose a multilayer filtering architecture, where each layer represents a Boolean or a stack filter and the outputs of the intermediate filtering layers provide some partial solutions for the optimization problem while the final solution is provided by the last layer output. The approach to the optimal design is based on a training framework. Simulations are provided to show the effectiveness of the proposed algorithms in image restoration applications.
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