The High Dynamic Range Compression Operator

the goal of classifying each pixel that lies on the surface of the object of interest as 1 and

The most powerful technique seen in modern video compression is described as inter-frame compression, and it allows for many of the pixels of a video to simply be “repeated”, rather than replaced. While pure static may never be the same frame-by-frame, most videos will have some captured colors that appear the same between each frame captured. In order to save space, most modern standards can tell a computer to simply ‘replicate’ that color, instead of create it once again, in a much more fluid process, requiring much less data. Modern MPEG-1 and MPEG-2

After finding K similar patches for each patch, the next step is to combine information from these patches to obtain a better estimate of the sparse representation of the HR patch. To compute a sparse representation for each patch , we need to remove its missing values, and the dictionary rows that are corresponding to the missing values. This can be done by a left matrix multiplication, similar to the previous section. Also, it is important to remove the mean intensity of each patch due to numerical stability issues [33]. We show the vector of observed pixels and the dictionary corresponds to it as , and , respectively. The columns of the must be normalized to unit one. This is a common practice to avoid scaling issues [39]. Thus, we

Abstract – In this paper, we proposed an effective system for lossless image compression using different wavelet transform such as the stationary wavelet transform, the non decimated wavelet transform, and discrete wavelet transform with delta encoding and compare the results without delta encoding. With the development in the field of networking in the process of sending and receiving files needed to effective techniques for image compression as the raw images required large amounts of disk space to defect during transportation and storage operations. We used lossless compression to maintain the original features of the image in this process; there is a major problem which often is low compression ratios. We also use three types of wavelet transform which are discrete wavelet transform, non decimated wavelet transform, and stationary wavelet transform to demonstrate the effectiveness of the proposed system for comparison and to show the differences between each type to overcome previous challenges. This paper proposed a system for lossless image compression based on the use of the three types of wavelet transforms using Arithmetic coding and Huffman coding with delta coding, which helps in the high compression ratio to clarify the extent of the difference and distinction between each type. The results show that the stationary wavelet transform outperforms the non decimated wavelet transform and the discrete wavelet transform and with delta encoding outperforms without delta

Tone mapping technique used to map high dynamic range images to low dynamic range image that will be displayed on recent devices, thus keeping the image looking realistic. The tone mapping operators can be classified into two categories: global operators and local operators. Global operator task depends on the same mapping function is used for all the pixels of the input HDR image so, global operators can be described as spatially uniform methods. Global tone mapping operators are computationally very simple and maintain the intensity orders of the original scenes thus avoiding ‘‘halo’’ artifacts, but cause loss of details in the mapped images. Local operators which reproduce the tonal values in a spatially variant manner because mapping function processes for each individual pixel. Local operators characterized that more satisfying than global operators in terms of the detail preservation and compression ratio of the dynamic range. Also, there are two tone mapping model linear tone mapping model and nonlinear tone mapping model. Linear model does not need multiscale decomposition or segmentation of the images into binary or fractional maps, where the problems associated with layer decomposition are not found. Linear model not only interested in making the output images looks natural by enhancing the pixel intensity more significantly in highlights, but also satisfies high radiance similarity with

Abstract— Wireless sensor networks (WSNs) is the collection of physical measurements in a geographical area. It tracks the spatial-average of the sensor measurements in a region. Since it is highly vulnerable to sensor faults and measurement noise the average operation is not robust. In this paper the proposed computational efficient method is used to compute a weight average of sensor measurement. It takes consideration of sensor faults and sensor noise. WSN uses random projections of sensor to compress data and send the compressed data to the data fusion center. The computation efficient method uses the data fusion center for direct work with the compressed data stream. The fusion center performed decompression at the time of computed weighted average. Thus, it reduces the computational requirements. Hence the proposed method gives better accuracy and more efficient for the WSN.

in a spatially variant manner because mapping function processes for each individual pixel. Local operators characterized that more satisfactorily than global operators in terms of the detail preservation and compression ratio of the dynamic range. Also, there are two tone mapping model linear tone mapping model and nonlinear tone mapping model. Linear model does not need multiscale decomposition or segmentation of the images into binary or fractional maps, where the problems associated with layer decomposition are not found. Linear model not only interested in making the output images look natural by enhancing pixel intensity more significantly in highlights, but also satisfies high radiance similarity with the ground truth HDR images. The problem appears when using linear model that usually fails when luminance value of a local patch changes abruptly. Also, the dynamic range of the dark region will be compressed at the same rate as the bright pixels, which causes losing details in this patch. Nonlinear model synchronize with the nonlinear relationship between the physical magnitudes of effects and the perceived intensity of the effects. Nonlinear model function depends on instead of linearly compressing the contrast of a patch in an input HDR image, it adopt the local gamma correction strategy which can not only effectively compress the bright pixels but also enhance the dark ones in one hit even in challenging patches Compared with the locally linear model, nonlinear

[x, y] = size (n) where x and y are the variables that are allocated with the max. coordinate values of the outlined image and n is the outlined image.

In Image Processing mostly the initial step will be the Image acquisition and smoothing. As the input for the tool of any Image Processing technique is an image, the input image should be taken and enhanced in all the ways possible. Enhancement involves smoothing the image, grey scaling, removing the unwanted blur, differentiating the subject from background and so on. In this project, for enhancing or smoothing the image we use the median filter. The median filter is non-linear digital filtering technique where the noise reduction is the pre-processing step before going to the further processing. Because the signal is big in the case of images, we chose median filter as it can handle the larger signal and the run-time is literally less.

The network settings are described in Table I, and represented as 64(9)-32(1)-32(5) - 32(3)-64(1)-1[9]-s. We used a strided convolution and deconvolution technique with stride s > 1 for network speed up. In our experiment we consider Y luminance color components only and the compressed luminance frames become input to MDCNN network. There is no max pooling or full-connected (FC) layers in our network. Rectified Linear Unit (ReLu) max (0, X) [19] is applied on the filter responses. Our CNN network is designed to learn the residue between output and input frame, as there is lots of similarity between them. So the final output frame F(Y) is just a combination of input frame and learnt residual frame.

’int’. Also, the pixel values were normalized to the range 0 and 1, and one hot encode the

Over several past years, contrast image enhancement has generated across many applications like robot sensing, electronic products, fault detection, medical image analysis, etc. Thus, increasing in popularity of contrast enhancement of images has forces researchers to study their enhancement techniques and their effectiveness for the interpretability or perception of human viewers. Contrast enhancement is a vital part of various fields, such as X-ray image analysis, biomedical image analysis, machine vision where pixel

Pixels having a value correspond to object and pixels with b value correspond to background. Usually, a=1 (white) and b=0(black) by convention. When T is a constant applicable over an entire image, the above equation is referred to as Global thresholding. In the algorithm, global thresholding concept is used. Enhanced image is converted into binary format by using the command im2bw and the resultant image is shown below

Assume that an image f(x,y) is sampled so that the resulting image has M rows and N columns so, the image size is M x N. The values of the coordinates are discrete quantities. The image origin is usually defined to be at (x, y) = (0, 0). The next coordinate values along the first row of the image are (x, y) = (0, 1). The notation (0, 1) is used to signify the second sample along the first row. It does not mean that these are the actual values of physical coordinates when the image was sampled. Fig. (5.3) shows this coordinate convention, where x ranges from 0 to (M-1) and y from 0 to (N-1) in integer increments [11].

With advent of modern high-performance signal processing applications, high throughput is in great demand. Digital Signal Processing is perhaps the most important enabling technology behind the last few decade’s communication and multi-media revolutions. Most recent research in the digital signal processing (DSP) area has focused on new techniques that explore parallel processing architectures for solutions to the DSP problems .DSP is used in a numerous real time application related with the VLSI technology such as wireless communication, transmission system, multimedia, digital video, digital audio and radar system. The field of DSP has always been driven by the advances in VLSI technologies. With the advances in