Progressive image coding by hierarchical linear approximation

The Laplacian pyramid is a versatile data structure that represents an image as a sequence of spatially filtered and decimated versions of the original image. To obtain an expanded approximation from the processed decimated samples, linear interpolation is commonly used due to its computational simplicity; however, linear interpolation does not consider the gray-level change in the neighborhood of each interpolated pixel, and thus creates unpleasant staircase effects near edges and visually annoying blocking effects in shade regions. This paper proposes a new pyramidal image coding algorithm using contour-based interpolative vector quantization (VQ) to obtain a perceptually better interpolated image through the use of edge information at each level of the pyramid. The basic idea is to code the image using VQ on a level-by-level basis in the hierarchical structure. The decimation implies that the redundancy existing over large image areas can be efficiently exploited by VQ of a low dimension. In order to improve the quality of the reconstructed image, an adaptive corrector stage is added at the final stage of the pyramid to selectively encode the residual errors as needed. The locations for correction are hierarchically predicted using the previously encoded data at the higher levels. The above scheme is a very efficient combination of VQ and contour-based interpolation in the hierarchical pyramid structure, and is amenable to progressive image transmission. Experimental results show that good reconstructed images are obtained at 0.25 bits per pixel with a reasonable coding complexity.

Adaptive Image Coding with Harmonic Angular Filters

A novel real-time and progressive secret image sharing with flexible shadows based on compressive sensing

This paper proposes a method that incorporates secret image sharing with progressive transmission based on the integer discrete cosine transform (IntDCT). The idea is to apply IntDCT coefficients after a combination process as input of the (r, n)-threshold model instead of directly sharing the secret pixels. A coarse version of the secret image can be decrypted by Lagrange interpolation with a small quantity of coefficients received. Moreover, the secret image is refined with more shared data received. It can be losslessly reconstructed as long as all coefficients received. Our method can be used for progressive share images transmission. Experimental results demonstrate the effectiveness of the proposed technique.

Image compression and progressive transmission (PIT) is a technique used as an alternative solution to the communication problem, where there is a need to transmit huge data such as in medical image transmission. The technique divides image that is to be transmitted into several phases and effectively provides a fairly accurate reconstruction of the original image in every phase. This paper proposes a novel architecture that combines bit-plane slicing, DD-dual-tree DWT, and SPIHT. The bit-plane slicing separates the image into eight planes; each bit plane is then decomposed with wavelet. The wavelet coefficients in different sub-bands are then encoded using SPIHT and transmitted progressively and reconstructed by combining the data received in each phase with the previous phase data. Experimental results have shown that the proposed technique has better efficiency. Even at the lowest SPIHT rate of 0.1 bits/pixel for a 512 × 512 gray scale medical image, the reconstructed image shows higher quality at third phase itself, which makes it an efficient choice for transmission of medical images even at low speed communication channel.

Image steganography is a technique for obscuring secret message within image pixel matrix. It provides means for secretly communicating between two entities with the third party totally unaware of the embedded message. One of the most important methods for image steganography is edge detection technique, wherein secret data is inserted in LSBs of image edge pixels. Data embedding in edge pixels does not create notable distortion in image when compared to embedding data in non-edge pixels. Different types of edge pixel detectors are available including most renowned Canny edge detector, Sobel edge detector, Prewitt edge detector and many others. Hybrid edge detection uses a combination of different edge detectors for generating edge pixels of image. Message to be secretly communicated is inserted in the LSB of these edge pixels, and the image thus formed called stego image is sent to the concerned party. Major drawback of hybrid edge detection technique is its capacity. The number of edge pixels is very less in any image. In the last decade, many papers have focused on increasing capacity of edge detection by using different techniques. In this work, we have proposed an extremely high-capacity hybrid edge detection technique by using data compression. Also, data is encrypted and then inserted in the cover image pixels. For message integrity, SHA512 hash function is used. Subsequently, this work not only improves capacity of edge detection while maintaining the image quality but also provides security and message integrity.

In this work, we investigate a new class of scalable image coders. We target at the same time multiresolution (for spatial scalability), critical (for compression efficiency) and (hierarchical) segmentation based decompositions (for object based scalability). Hierarchical segmentation allows to access the description of a scene in terms of regions or objects at several resolution levels, and thus encode and transmit the objects selectively. From a coding viewpoint, it is obviously interesting to couple the multi-level segmentation with a critically decimated decomposition of the image (to avoid redundancy of representation). However, the association of object representation combined with critically sampled multiresolution decomposition has not been studied to our knowledge. In this paper, we propose new methods to perform hierarchical segmentation of an image using critically decimated non linear filter banks; the resulting decomposition embeds a hierarchical segmentation map and is therefore particularly well suited for region based coding and progressive transmission. As the segmentation map is embedded by reconstruction inside the decomposition, we do not really need to transmit it separately, thus attempting to reduce the bitrate. Simulations show that a prototype coder of this type has a degradation in terms of rate/distortion tradeoff compared to a conventional wavelet based image coder, but offers in addition new perspectives for object based manipulations, coding and transmission.

Digital medical images are becoming even more widespread and used in a large variety of applications. Such images are often stored in local repositories and need to be transmitted/ received over the network. Therefore, data compression is an essential mechanism to adopt for improving the transmission time, as well as to optimize the required storage space. However, due to the clinical relevance of such data, lossless techniques are almost ever preferred, given that in such context it is not tolerated any loss of data. In this work, starting from the Medical Images Lossless Compression (MILC) algorithm, which enables the efficient coding of three-dimensional medical images achieving results comparable with the other state-of-art algorithms, we propose Parallel MILC, a fully parallelized version of that algorithm, which provides some attractive features with respect to MILC, especially in terms of execution speedup and scalability. In detail, we propose a novel design and implementation for the MILC algorithm, which is able to exploit the power and the capabilities of the parallel computing paradigm. It is important to point out that Parallel MILC can be executed on several heterogeneous device types supporting OpenCL (i.e. CPU, GPU, FPGA, etc.). The preliminary test results show a significant performance speedup of Parallel MILC compared to MILC. Consequently, the novel algorithm we propose provides MILC with strong scalability properties and complete transparency with respect the underlying hardware.

Numerous sophisticated techniques have been developed over the last several decades to efficiently transmit images across noisy channels. Here, we cascade an existing image coder with carefully chosen error control coding, and thus produce a progressive image compression scheme whose performance on a noisy channel is significantly better than that of previously known image compression techniques. The main idea is to trade off the available transmission rate between source coding and channel coding in an efficient manner. This coding system is easy to implement and has acceptably low complexity. Furthermore, effectively no degradation due to channel noise can be detected; instead, the penalty paid due to channel noise is a reduction in source coding resolution. As an example, for the 512/spl times/512 Lena image, at an overall transmission rate of 1 bit per pixel, and for binary symmetric channels with bit error probabilities 10/sup -3/, 10/sup -2/, and 10/sup -1/, the proposed system typically outperforms other existing systems by at least 2.6 dB, 2.8 dB, and 8.9 dB, respectively.

Matrix newton interpolation and progressive 3D imaging: PC-based computation

The relay node placement problem in wireless sensor network (WSN) aims at deploying the minimum number of relay nodes over the network so that each sensor can communicate with at least one relay node. When the deployed relay nodes are homogeneous and their communication ranges are circular, one way to solve the WSN relay node placement problem is to solve the minimum geometric disk cover (MGDC) problem first and place the relay nodes at the centers of the covering disks and then, if necessary, deploy additional relay nodes to meet the connection requirement of relay nodes. It is known that the MGDC problem is NP-complete. A novel linear time approximation algorithm for the MGDC problem is proposed, which identifies covering disks using the regular hexagon tessellation of the plane with bounded area. The approximation ratio of the proposed algorithm is (5+ϵ), where0<ϵ≤15. Experimental results show that the worst case is rare, and on average the proposed algorithm uses less than 1.7 times the optimal disks of the MGDC problem. In cases where quick deployment is necessary, this study provides a fast 7-approximation algorithm which uses on average less than twice the optimal number of relay nodes in the simulation.

Image Coding and Data Compression

Increasing image compression rate using steganography

Image Coding and Data Compression

Modelling a 2-D inverse fast cosine transform algorithm on a multistage network