Coding Of Binary Images Research Articles

Analysis of Binary Image Coding Methods for Outdoor Applications of Wireless Vision Sensor Networks

The processing of images at the vision sensor nodes (VSN) requires a high computation power and their transmission requires a large communication bandwidth. The energy budget is limited in outdoor applications of wireless vision sensor networks (WVSN). This means that both the processing of images at the VSN and the communication to server must be energy efficient. The wireless communication of uncompressed data consumes huge amounts of energy. Data compression methods are efficient in reducing data in images and can be used for the reduction in transmission energy. We have evaluated seven binary image coding techniques. Our evaluation is based on the processing complexity and energy consumption of the compression methods on the embedded platforms. The focus is to come up with a binary image coding method, which has good compression efficiency and short processing time. An image coding method with such attributes will result in reduced total energy requirement of the node. We have used both statistically generated images and real captured images, in our experiments. Based on our results, we conclude that International Telegraph and Telephone Consultative Committee Group 4, gzip_pack and JPEG-LS are suitable coding methods for the outdoor applications of WVSNs.

An automatization of Barnsley's algorithm for the inverse problem of iterated function systems

We present an automatization of Barnsley's manual algorithm for the solution of the inverse problem of iterated function systems (IFSs). The problem is to retrieve the number of mappings and the parameters of an IFS from a digital binary image approximating the attractor induced by the IFS. M.F. Barnsley et al. described a way to solve manually the inverse problem by identifying the fragments of which the collage is composed, and then computing the parameters of the mappings (Barnsley et al., Proc. Nat. Acad. Sci. USA, vol.83, p.1975-7, 1986; Barnsley, "Fractals Everywhere", Academic, 1988; Barnsley and Hurd, L., "Fractal Image Compression", A.K. Peters, 1992). The automatic algorithm searches through a finite set of points in the parameter space determining a set of affine mappings. The algorithm uses the collage theorem and the Hausdorff metric. The inverse problem of IFSs is related to the image coding of binary images. If the number of mappings and the parameters of an IFS, with not too many mappings, could be obtained from a binary image, then this would give an efficient representation of the image. It is shown that the inverse problem solved by the automatic algorithm has a solution and some experiments show that the automatic algorithm is able to retrieve an IFS, including the number of mappings, from a digital binary image approximating the attractor induced by the IFS.

An evolutionary algorithm for fractal coding of binary images

An evolutionary algorithm is used to search for iterated function systems (IFS) that can encode black and white images. As the number of maps of the IFS that encodes an image cannot be known in advance, a variable-length genotype is used to represent candidate solutions, Accordingly, feasibility conditions of the maps are introduced, and special genetic operators that maintain and control their feasibility are defined, In addition, several similarity measures are used to define different fitness functions for experimentation. The performance of the proposed methods is tested on a set of binary images, and experimental results are reported.

Skeleton-based morphological coding of binary images

This paper presents new properties of the discrete morphological skeleton representation of binary images, along with a novel coding scheme for lossless binary image compression that is based on these properties. Following a short review of the theoretical background, two sets of new properties of the discrete morphological skeleton representation of binary images are proved. The first one leads to the conclusion that only the radii of skeleton points belonging to a subset of the ultimate erosions are needed for perfect reconstruction. This corresponds to a lossless sampling of the quench function. The second set of new properties is related to deterministic prediction of skeletonal information in a progressive transmission scheme. Based on the new properties, a novel coding scheme for binary images is presented. The proposed scheme is suitable for progressive transmission and fast implementation. Computer simulations, also presented, show that the proposed coding scheme substantially improves the results obtained by previous skeleton-based coders, and performs better than classical coders, including run-length/Huffman, quadtree, and chain coders. For facsimile images, its performance can be placed between the modified read (MR) method (K=4) and modified modified read (MMR) method.

Binary image representation and coding by a double-recursive morphological algorithm

This paper presents a recursive morphological operation which can be advantageously substituted to classical erosion with an application for image coding. A structuring element is translated over binary image following four scanning modes. The result of the operation, as for classical erosion, corresponds to the centers of the structuring elements which are included in the image objects, but our operation authorizes only a minimal overlap between the translated structuring elements. This minimal overlap needs to take into account previous erosions, so the operation is recursive. It results in a subset of the erosion, containing fewer points than the erosion, but gives a good approximation to image objects. In a second step, operation is extended with a second recursivity level corresponding to structuring element size variation. This new double-recursive morphological algorithm describes an image object, from coarser structures to finer details, by the loci and sizes of the translated structuring elements which are included in the object. This algorithm provides a minimal redundancy shape representation. To evaluate this algorithm in the context of binary image coding, we propose two different techniques for encoding representative points issued from our shape decomposition algorithm. Comparative experimental results with two classical methods (Modified Huffman RLC and TUH code) and also a morphological method (discrete skeleton), expressed in terms of compression ratio, show the good performances of our approach.

Morphological shape and region description

Abstract A new algorithm for multiscale description of binary digital regions is given. A region is represented by a growing sequence of subsets approximating it; each approximation is obtained from the previous one by addition of the opening by a structuring element chosen in a finite family. The structuring elements vary in size and shape, and are chosen at each step according to two criteria: largest structuring element size and greatest increment in the size of the approximation. The algorithm satisfies meaningful algebraic properties and converges to a final result in a bounded number of steps. It also allows an economical representation of shape in terms of structuring elements, and so it can be applied in binary image coding.

Morphological shape decomposition

A technique for decomposing a binary shape into a union of simple binary shapes is presented. The decomposition is shown to be unique and invariant to translation, rotation, and scaling. The techniques used in the decomposition are based on mathematical morphology. The shape description produced can be used in object recognition and in binary image coding.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Erosion and dilation of binary images by arbitrary structuring elements using interval coding

Interval coding of binary images provides a representation in which the mathematical morphology operations of dilation and erosion by an arbitrary structuring element can be naturally and efficiently implemented on a serial computer. Conventional 4- or 8-neighbour dilation and erosion are then only special cases. Euclidean mapping is investigated as an example.

Parallel coding of binary images

Due to the large amount of data to be manipulated in image processing, efficient coding methods for data compression are necessary both for the execution of algorithms, and for transmission and storage of pictorial data. While for sequential computers many coding methods have been developed, they have been less investigated so far for parallel computers. In the present work parallel coding algorithms, mainly based upon heuristics, are developed. Great importance is attributed to data compression of binary images for filing or transmission on the one hand, and to description of the underlying stochastic scenes (from material science, biology ...), in terms of a classification by size and shape of the components of the image on the other hand.

Morphological skeleton representation and coding of binary images

This paper presents the results of a study on the use of morphological set operations to represent and encode a discrete binary image by parts of its skeleton, a thinned version of the image containing complete information about its shape and size. Using morphological erosions and openings, a finite image can be uniquely decomposed into a finite number of skeleton subsets and then the image can be exactly reconstructed by dilating the skeleton subsets. The morphological skeleton is shown to unify many previous approaches to skeletonization, and some of its theoretical properties are investigated. Fast algorithms that reduce the original quadratic complexity to linear are developed for skeleton decomposition and reconstruction. Partial reconstructions of the image are quantified through the omission of subsets of skeleton points. The concepts of a globally and locally minimal skeleton are introduced and fast algorithms are developed for obtaining minimal skeletons. For images containing blobs and large areas, the skeleton subsets are much thinner than the original image. Therefore, encoding of the skeleton information results in lower information rates than optimum block-Huffman or optimum runlength-Huffman coding of the original image. The highest level of image compression was obtained by using Elias coding of the skeleton.

Efficient implementation of skeletonisation using interval coding

Interval coding of binary images provides a representation in which operations such as AND, OR, XOR, dilation and erosion can be efficiently implemented on a serial computer. Using these procedures, various alternative schemes for implementing Hilditch's skeletonisation method efficiently are investigated.

Neighborhood coding of binary images for fast contour following and general binary array processing

The problem of computing 3 × 3 neighborhood functions of binary images can be subdivided into that of accessing the eight neighbors of a pixel and assembling them into a compact 8-bit code, and then of computing the desired neighborhood function (s) by simple table-lookup on the code. When the task is looked at in this way, it is apparent that much, if not most, of the computational effort on a serial computer goes into the accessing and assembling of the neighbors of a pixel. We propose a simple hardware encoder to handle this part of the task, and thus allow for significant speedup of the overall computation. We shrew in particular how this coding and encoder can be applied to the problem of boundary contour following, and suggest other areas of application.