Analysis Of HEVC Intra Networking

The objective of the neural network is to transform the input to meaningful output. Neural networks are often used for statistical analysis and data modeling. Neural network has many uses in data processing, robotics, and medical diagnosis [2]. From the starting of the neural network there are various types found, but each and every types has some advantages and disadvantages. Deep learning and -neural network software are the categories of artificial neural network. The parallel process also allows ANNs to process the large amount of data very efficiently. The artificial neural network is built with a systematic

Shi Et al [13] used a local projection profile at each pixel of the image, and transform the original image into an adaptive local connectivity map (ALCM). For this process, first the gray scale image is reversed so the foreground pixels (text) have idensity values of up to 255. Then the image is downsample to ¼ of each size, ½ in each direction. Next a sliding window of size 2c scans the image from left to right, and right to left in order to compute the cumulative idensity of every neighborhood. This technique is identical to computing the projection profile of every sliding window, (i.e. counting the foreground pixels), but instead of outpiting a projection profile histogram, the entire sliding window sum is saved on the ALCM image. Finaly

Has (CMOS) sensor to provide clear video and clear still images with 1920 x 1080 resolution. [2]

being attacked, encrypt it with a secret key to obtain an encrypted bit stream . For that AES encryption is used. After AES encryption, will embed at the fourth bit position of . Some related information about the mosaic image generation process into the mosaic image for use in the secret image recovery process are also embed at the last fourth bit position of . Such information, described as a bit stream I like M, includes the following data items: 1) encryption key; and 2) total length of before encryption. After all this process Target image with mosaic image, residual value and all information that required to recover original image are transmitted to receiver

Table I displays six solid binary images along with their corresponding compression ratios. On considering Run-Length coding, the alternative encoding scheme produces better performance than the other encoding scheme. On an average, the proposed algorithm performs well on comparison with the standard Huffman coding algorithm by approximately 1.58%.

Seen as there are numerous different uses for images it means that the quality will differ for different purposes. The formats which I will be discussing within this report include JPEG, GIF’s, TIF, PSD and BMP. All these formats are made for different reasons and I will be explaining these differences in quality and file size. Each format varies in the depth of the colour within the image, some have larger colour depth meaning that the file size will be larger, whereas, some of these formats have very low colour depth resulting in a lower quality image. When exporting an image, sometimes there is an option to adjust the colour depth to your preference, so, the file format could change to your desired colour depth.

The luminance histogram of a exemplary natural scene that has been linearly quantized is commonly highly skewed toward the darker levels; a majority of the pixels possess a luminance lower than the average. In similar images, detail in the darker regions is often not visible. One means of enhancing these types of images is a method called histogram modification, in which the original image is rescaled so that the histogram of the intensified image follows some desired form [6]. This method also assumes the detail carried by an image is related to the possibility of occurrence of each gray level. To maximize the detail, the transformation should redistribute the possibilities of occurrence of the gray level to make it identical. In this way, the contrast at every gray level is proportional to the altitude of the image histogram [7]. Several modifications of histogram equalization are also available which expansion its potential of contrast enhancement. Adaptive histogram equalization (AHE) [8] and Contrast limited adaptive histogram equalization (CLAHE) [9] belong to that classification which apply histogram

For learning image processing, It is better to know grayscale processing firstly and next recognize how it applies to multichannel processing before starting the full color imaging. By learning single channel processing we can miss all important insights.

embedded in previous frames should propagate into the modern frame and create visible distortion. The drift compensation signal accounts for these previously embedded watermarks and remove them from the current frame. Finally, if a bit-rate constraint is desired, an additional step is required. Let n0 denote the number of bits for the compressed, unmarked

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

In this chapter, we will discuss our experimental results. The aim of this experiment to find more accurate results for remote sensing image scene classification. In this experiment we have used NWPU-RESISC45 dataset. We applied some normal learning procedure and deep learning procedure in this experiment. In deep learning procedure we used two well-known deep learning named VGG16 and ResNet50. We, trained them from scratch as well as applied transfer learning on our dataset. We discussed about the procedures in the previous chapter. We will discuss the results, make comparison and eventually draw a conclusion on this chapter. We will also discuss about implementation environment and dataset.

“Video texture” is a new media introduced by Schodl et al.[23] and it is simply a video that plays forever, which can be used as a picture that moves. The problem areas associated with video texture generation are similarity measurement between frames or regions within frames, segmentation of re- gions, and the

Because of the great interest of HDR images and their advantages, tone mapping techniques are used to display these images on LDR devices. Many researchers made different studies for evaluating the performance of tone mapping algorithms. Debevec and Malik proposed that a HDR image can be created from three or more LDR images of the same scene under different exposures. Through technological advances and the development of photograph technology, we can get access to HDR cameras which can take HDR photos and videos directly now. Therefore, there is an increasing demand for tone mapping algorithms. There are two types of tone mapping algorithm: global operators and local operators. The global tone mapping methods are simply mapped the input HDR image I^h (x,y) to an output LDR image I^l (x,y) = f (I^h (x,y)) , where f () is a global compression function which is spatially invariant, such as gamma function, linear function, histogram based function and the function adapted to tone reproduction curves. Hence, the local tone mapping methods decompose the input HDR image into multiple layers or areas, apply different compressing algorithm in different layers or areas and recombine all the layers or areas into a LDR image. Similar with Durand, Farbman which segmented the HDR image into two layer a base layer and a detail layer, while the base layer is obtained by applying an alternative edge-reserving smoothing operator and the detail layer is obtained by

In this paper, new adjustment tone mapping method is presented for displaying high dynamic range image on low dynamic range device without any lose in image details and preserves image contrast. Instead of linearly scaling the luminance of all pixels or introduce local gamma correction with adaptive parameters for all pixels, the paper core idea is using linear operator in specific pixels and using nonlinear operator in another pixels according to guidance map. The optimal compression can finally be achieved by solving the optimization problem that integrates all window-based constraints which can be transformed to a sparse linear equation.

Abstract: The original digital speech signal contains tremendous measure of memory, the main concept for the speech compression algorithm is presented here, in which bit rate of the speech signal is reduced with maintaining signal quality for storage, memory saving or transmission over the long distance. The concentration of this project is to compact the digital speech signal using Discrete Wavelet Transform and reconstruct same signal using inverse transform, in .NET. The algorithm of Compression is oriented in three basic operation, they are apply the DWT, Threshold, Encode the signal for transmission Analysis of compression procedure is done by comparing the original speech and reconstructed signal. The main advantages of DWT provides variable compression factor.