The orientation adaptive lapped orthogonal transform for image coding

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

Adaptive Image Coding with Harmonic Angular Filters

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.

Summary form only given. Block-based Discrete Cosine Transform (BDCT) has been widely used in image and video compression due to its energy compacting property and relative ease of implementation. However, BDCT has a major drawback, which is usually referred to as blocking artifacts. Blocking artifacts appear as grid noise along the block boundaries because each block is transformed and quantized independently. Image deblocking techniques can reduce these distortions and alleviate the conflict between bit rate reduction and visual quality preservation. Many state-of-the-art image deblocking algorithms treated the blocking artifacts reduction of the compressed image as an inverse restoration problem. Natural image prior models are well utilized into the blocking artifacts reduction processing, such as the local sparsity prior model and non-local similarity property of natural images. These two local and non-local models characterize the image prior information in two complementary perspectives. Therefore, it is necessary to combine these two models in a unified framework. In this paper, we propose a novel method to reduce the blocking artifacts of blockcoded images via patch clustering and low-rank minimization, which simultaneously exploits the local and non-local sparse representations in a unified framework. First, the whole compressed image are divided into small patches. For each patch, we perform patch clustering to collect similar patches into a group. Then the whole group are simultaneously reconstructed by a low-rank minimization approach. Singular value thresholding (SVT) algorithm is employed to solve the low-rank minimization problem. To further improve the performance of the proposed algorithm, we adopt an iterative procedure to utilize the newly output data in each iteration and update the noise and signal variance adaptively. Experimental results show that the proposed method achieves higher PSNR and SSIM than the state-of-the-art methods. Comparing to the state-of-theart algorithms and, the proposed algorithm achieves about 0.37dB and 0.11dB improvement on average. For visual quality assessment, the deblocking images produced by the proposed algorithm reveal much more sharp edge structures and richer textures.

It is well known that at low bit rates, a block-based discrete cosine transform compressed image or video can exhibit visually annoying blocking and ringing artifacts. Low-pass filters are very effective in reducing the blocking artifacts in smooth areas. However, it is difficult to achieve a satisfactory result for ringing artifact removal using only an adaptive filtering scheme. This paper presents a neural network-based deblocking method that is effective on various types of images. The first step of this scheme is block classification that identifies each 8 × 8 block as one of the three types: PLAIN, EDGE or TEXTURE, based on its statistical characteristics. The next step is the reduction in the blocking and ringing artifacts by applying three trained layered neural networks to three different types of image areas. Comparing this method with other algorithms, the simulation results clearly show that the proposed algorithm is very powerful in effectively reducing both blocking and ringing artifacts while preserving the true edge and textural information and thus significantly improving the visual quality of the blocking images or videos.

Coding of images or processing of images has played a momentous role in the success of digital communications and multimedia. Coding plays a major role is 3D mobile generation. The image processing methods have been exploited through the years to improve the quality of digital images. In the image processing system De-noising based coding is used extensively. As we know that, in low bit rate applications, the blocking artifacts problem arises, which severely reduce the visual quality of the image. Reducing blocking artifacts is essential to render the compressed visual data acceptable to the viewer. So, to detect and reduce noise from the image is a major task. So removing the noise from original signal is still a challenging problem for researchers. There have been several published algorithms and each approach has its assumptions, advantages, and limitations. For real-time applications like television, photo-phone, etc. it is essential to reduce the noise power as much as possible and to retain the fine details and the edges in the image as well. Moreover, it is very important to have very low computational complexity so that the filtering operation is performed in a short time for online and real-time applications. The main focus of this paper is first to define an algorithm and based on that a non linear threshold filter is described which not only preserve the actual image structures in the presence of different types of noises (Salt & peepers, Speckle and Gaussian Noise) as well as it provide better PSNR, MSE, MAE, & Time Complexity than others Classical algorithms.

Weighted roll-off scheme to remove block artifacts for low power local dimming liquid crystal displays

An adaptive image steganography using AMBTC compression and interpolation technique

Recently, a heat source system utilizing a heat storage tank for energy savings in buildings was designed. A heat storage tank is an effective system for solving the qualitative and quantitative differences in the required building energy. On the other hand, the existing design process of a heat storage system is difficult to determine if the air-conditioning time is unclear, and the design in a real-working level is too inaccurate, causing oversizing and a high initial investment cost. This results in inefficient operation despite the introduction of an efficient system. Therefore, this study proposes an optimal design method of a heat source system using a thermal storage tank. To demonstrate the usefulness of the proposed design method, feasibility studies were conducted with the existing system designs. As a result, the optimal solution could reduce the initial cost by approximately 25.6% when following the conventional design process and it was approximately 40% lower than the real-working method. In conclusion, the conventional designs are inefficiently over-designed and the optimal design solution is superior. In this regard, the suggested optimal design method is efficient when designing a heat source system using a thermal storage tank.

The purpose of the paper is to improve the state-of-the-art Syndrome Trellis Code (STC) methodology and use it along with adaptive steganographic approach to improve the security related aspects of STC steganographic systems. Hence the goal is to provide improved security by making minimal changes to the cover image and thus prevent the intruder from detecting the presence of message in it. This work concentrates on research, analysis, design and implementation of efficient Adaptive Image Steganography systems based on Syndrome-Trellis Code.

When a high compression ratio is required, the block discrete cosine transform (BDCT) decoded image suffers from the blocking effect: boundaries between adjacent image blocks visible for low bit rates. A blocking effect reduction algorithm based on DCT transform and Markov random field (MRF) is proposed to reduce blocking artifacts as many as possible, while preserving edge information. In this paper, the characteristic of human visual system (HVS) is sufficiently utilized. A visibility function of blocking artifacts is proposed based on the activity masking and brightness masking properties of HVS. The blocks which visibility of the blocking artifacts is less than the threshold do not need any processing. The proper threshold value for Huber function plays an important role in MRF method. The linear regression is used to adaptively estimate the threshold value. Experimental results show the proposed algorithm can significantly reduce blocking artifacts while preserving edge and texture information

Transform coding is the most popular image compression method. However, for high compression it produces noticeable blocking artifacts in the decoded image. This paper presents new algorithms for reducing blocking artifacts. The algorithms use piecewise self-similarity within different parts of the image as a priori to give reasonable modification to the block boundary pixels. The proposed postprocessing algorithm includes block classification in transform coefficient domain, and each block boundary area smoothing is performed by searching similar blocks. In experiments it outperforms an existing method and gives significant subjective and objective improvement over the decoded image.

Based on regularization principles, we propose a new dequantization scheme for MPEG. The new approach drastically reduces blocking artifacts without smoothing the decoded image. Most discrete cosine transform (DCT) based video coding suffer from blocking artifacts where boundaries of 8/spl times/8 DCT blocks become visible on decoded images. The blocking artifacts become more prominent as the bit rate is lowered. We present a new dequantization technique for discrete cosine transform (DCT) based encoding to sharply reduce the blocking artifacts. Our proposed dequantization scheme, through regularization, sharply reduces blocking artifacts in decoded images. The performance of the proposed algorithm is evaluated against the standard MPEG decoding. The comparison shows visual improvements as well as numerical improvements in terms of the peak-signal-to-noise ratio (PSNR) and the blockiness measure (BM).

Fast Image Restoration for Reducing Block Artifacts Based on Adaptive Constrained Optimization

We propose a modified overlapped block-matching (OBM) scheme for stereo image coding. OBM has been used in video coding but, to the best of our knowledge, it has not been applied to stereo image coding to date. In video coding, OBM has proven useful in reducing blocking artifacts (since multiple vectors can be used for each block), while also maintaining most of the advantages of fixed-size block matching. There are two main novelties in this work. First, we show that OBM techniques can be successfully applied to stereo image coding. Second, we take advantage of the smoothness properties typically found in disparity fields to further improve the performance of OBM in this particular application. Specifically, we note that practical OBM approaches use noniterative estimation techniques, which produce lower quality estimates than iterative methods. By introducing smoothness constraints into the noniterative DV computation, we improve the quality of the estimated disparity as compared to standard noniterative OBM approaches. In addition, we propose a disparity estimation/compensation approach using adaptive windows with variable shapes, which results in a reduction in complexity. We provide experimental results that show that our proposed hybrid OBM scheme achieves a PSNR gain (about 1.5-2 dB) as compared to a simple block-based scheme, with some slight PSNR gains (about 0.2-0.5 dB) in a reduced complexity, as compared to an approach based on standard OBM with half-pixel accuracy.