Adaptive Image Coding with Harmonic Angular Filters

The growth in the volume of information transmission and transaction over the internet has necessitated the development and enhancement of measures and techniques required to ensure the safety of critical information during transmission. with developments in mobile technologies and the adoption and diffusion of android mobile devices, research must therefore continue to explore more secured media in which information can be hidden. One of such medium is the use of image carriers referred to as steganography. In tis paper, we provide insughts into the development of an android-based Image Steganography System for concealing data and information using adaptive image steganography. The Least Significant Bit (LSB) algorithm was employed as the algorithmic sequence for the development. Preliminary results from implementation and testing showed promising results Keywords: Android, image steganography, data, information, transmission, images, security

Progressive image coding by hierarchical linear approximation

Low-complexity progressive image transmission scheme based on quadtree segmentation

A lapped orthogonal transform adapted for local directionality of an image is proposed and its application in adaptive image coding is presented. First, we address the problem of constructing the lapped orthogonal transform with short and long functions such that the long functions are twice the length of the short functions, and derive the transform with the optimal short functions in the energy compaction sense. Moreover, using the optimal solution, we propose the concept of an orientation adaptive lapped orthogonal transform (OALOT) and give its design method. Experimental results show that in addition to the property of the LOT which reduces blocking artifacts, the OALOT possesses the property of preserving strong edges and lines.

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.

Current progressive image transmission (PIT) systems can not control both bit rate and resolution constraints in images. Due to the partially localised nature of the wavelet transform, the value of any pixel in the image depends on only a small number of wavelet coefficients. Thus, it is possible to specify an arbitrary region of the image and prevent that region from being badly degraded during the compression process. This can be done by representing coefficients corresponding to those regions in the wavelet space more accurately. This is accomplished by multiplying those values by an arbitrary factor before applying the embedded zerotree wavelets (EZW) coding technique. The EZW algorithm is then effectively applied to, wavelet coefficients to refine and encode the most significant ones in the wavelet space. The information of the image is thus transmitted in several successive stages. It is shown that in a PIT system not only any region of interest (ROI) can be sent and received progressively, but also bitrate and resolution constraints can be controlled simultaneously. This option becomes very effective in interactive communication channels where the available bandwidth is at a premium.

Few networks offer sufficient bandwidth for the transmission of high resolution two- and three-dimensional medical image sets without incurring significant latency. Traditional compression methods achieve bit-rate reduction based on pixel statistics and ignore visual cues that are important in identifying visually informative regions. This paper describes an approach to managing image transmission in which spatial regions are selected and prioritized for transmission so that visually informative data is received in a timely manner. This context-based image transmission (CBIT) scheme is a lossless form of progressive image transmission (PIT) in which gross structure, represented by an approximate iconic image, is transmitted first. Each part of this iconic image is progressively updated, using a simple set of rules that take into account viewing requirements. CBIT is realized using knowledge about image composition to segment, label, prioritize, and fit geometric models to regions of an image. Tests, using neurological images, show that, with CBIT, a valuable transmitted image is received with a latency that is about one-tenth that of traditional PIT schemes. Frequently, the necessary regions of the image are transmitted in about half the time taken to transmit the full image.

Adaptive image coding scheme are well adapted to multirate cognitive networks. A Cognitive device will be able to reconfigure itself to adapt its baseband radio transmitter to the required bit rate of the coding scheme. In this paper, a such platform able to adapt the radio access technology (RAT) to the image coding scheme is described. To optimize the RAT bit rate to the image coding bit rate, our system chooses and reconfigures in real time the radio baseband functions. It makes some cross-layer adaptation between PHY and application layers.

Pyramid data structures have played an important role in progressive image transmission. Over the years, the reduced difference pyramid (RDP) has been found to be one of the best data structure. The RDP takes the differences between the neighboring nodes at the same level. The new modified difference pyramid (MDP) data structure, developed in this paper, takes the differences between successive levels. Simulation results show that both the bit rate and the complexity of the receiver for the MDP structure are lower than those for the RDP structure at the same quality. An MDP coding process which incorporates a prefilter is also proposed in this paper. Simulation results show it can provide good quality (in a subjective sense) reconstructed images at a lower bit rate than the unfiltered scheme can. Also, acceptable intermediate images are interpolated via the repetition method and via the cubic convolution method to get images which are the same size as the original. Results are compared.

A new vector quantization technique, called storage- and entropy-constrained multi-stage vector quantization (SECMSVQ) technique, is presented for progressive image transmission (PIT) applications. In the SECMSVQ technique, the rate constraint at each stage of the vector quantizer (VQ), and the storage size of the whole VQ are specified independently before the design. A dynamic programming technique is then used to allocate the storage size to each stage of the VQ optimally so that the quantization distortion of the VQ is minimized. The SECMSVQ is well-suited to constructing a PIT system where the transmission time and storage size are the major concerns. Moreover, simulation results show that the SECMSVQ technique can be used effectively to implement signal compression systems under the storage and rate constraints.

Medical images are of high volume and contain valuable diagnostic information, which have to be preserved against any archival or transmission error. Progressive transmission of data is favourable due to the fact that the region of interest (RoI) within the image can be coded without any visible error and that it speeds up the transmission of desired information due to the hierarchical region relation & transmission. In addition, this modality of communication has been modified to be interactive so the physicians can select the RoI to prioritize transmission of various regions. Wireless transmission of medical images involves construction of an effective source-channel coding to not only preserve the diagnostic information but also to enable progressively streaming the data from host to receiver [1]. A number of large medical images have been compressed and progressively transmitted with low bitrate. The blocking effect, as can be seen in JPEG coded images with the same compression ratio, has been highly reduced and the quality of the decoded images enhanced. A MATLAB-based TCP/IP connection has been established to demonstrate the proposed interactive progressive transmission system.

In this paper a new developed algorithm for compression of still images based on 2D-SOFM NN's in correspondence with the method of Inverse Difference Pyramid (IDP) decomposition is represented. The new developed algorithm is well suited to be used in Progressive Image Transmission (PIT). Advantage of the method relies on the learning process and adaptation capability of NN's to reduce the matrices computation complexity and the total number of pyramid levels required for PIT. In addition to, for image reconstruction no interpolation is needed anymore, which improves the quality of the reconstructed image.

An efficient coding scheme for progressive transmission of gray-scale images is presented. In the scheme, a rough sketch of an image is transmitted first and the details are added later. At the final stage of transmission, a receiver can reproduce the exact replica of an original image with high coding efficiency. This kind of presentation is especially desirable for the interactive visual search of large image databases, where a viewer can recognize image content as quickly as possible, halt transmission of unwanted detail, and retry another image. This scheme utilizes subsampling and level-plane coding in a recursive way. Thus, the coding algorithm is simple and can be easily implemented even on a personal computer. Computer simulations show that the scheme achieves the mean code length of 5.24 b/pel for an image, which is almost the same as that of efficient nonprogressive coding schemes. >

In this paper, joint source-channel decoding (JSCD) methods are designed for JPEG2000 progressive image transmission over both AWGN and flat fading channels. A regular low-density parity-check (LDPC) code is selected as the error correcting code. Error resilience mode is used in the JPEG2000 codec. A framework for the density evolution analysis is given to show its validation for the proposed JSCD methods. A general modification algorithm that utilises feedback information from the channel decoder is proposed to update the log-likelihood ratio (LLR) of partial codeword bits. Simulation results show that the JSCD design accelerates the iterative LDPC decoding procedure while the overall performance in terms of the PSNR for reconstructed images outperforms the non-joint decoding approach.

In this paper, a hybrid coding scheme for progressive image transmission (PIT) based on two components, that is the smooth and edge components, is presented. The image is first decimated by using the pyramid coding technique with a decimation factor of 4:1. A multistage predictive locally adaptive vector quantization (MSPVQ) progressively transmits the decimated image that represents the smooth component of the scene. The first stage of the proposed (VQ) gives a general grasp of the Lena image at a compression ratio as high as 80:1. An adaptive VQ (LAVQ) encodes the difference image, which contains the quantization and edge error of the smooth component. By adding the encoded image components, an excellent reproduction is achieved at a low bit rate of only 0.2 bit per pixel (bpp), i.e. compression ratio of 40:1, with peak signal-to-noise ratio (SNR) of 36.92 dB. It is also shown that, for the Lena image and for the same PSNR of about 34 dB, the bit rate using the proposed algorithm is very low; only 0.2 bpp. This bit rate is lower than about 0.6 bpp using the JPEG algorithm, and much lower than that using BTC-DCT or SB-DCT algorithms. Thus the proposed algorithm results in a saving of about 0.4 in the bit rate for the same visual quality.