Efficient Video Compression Schemes

The wide use of images and videos in the day to day communication in recent trends made video compression a significant feature in information transmission and social networking. Also the limited bandwidth for transmission and limited memory make it a serious phenomenon to consider. There is a need to improve the video encoding process which can encode the video data with low computational complexity and high compression ratio along with maintaining quality. Motion Estimation (ME) is the widely used scheme in many encoders like MPEG-2, MPEG-4 and H.264 that removes temporal redundancy. In this thesis, many ME algorithms like Full Search, Logarithmic Search, Diamond Search, Cross Diamond Search, Kite Cross Diamond Search (KCDS), Hexagonal Search (HEXS), Enhanced Hexagonal Search (ENHEXS) etc. are implemented and analysed. Based on the conclusions drawn modifications to KCDS and HEXS are proposed by imparting the concept of motion vector prediction. A novel Hybrid Hexagonal Kite Cross Diamond Search (HYBHKS) algorithm is proposed. It has the capability of adaptive switching of search patterns based on the type of motion of the block that can reduce the computational complexity of the encoder maintaining quality. Analysis is done on the impartation of Discrete Hartley Transform in video encoding. Video Compression may still be increased by reducing the information to be encoded. This is done by skipping some irrelevant information. In this thesis, compression techniques involving the concepts of spatial and temporal correlation of rows are implemented where only alternate rows of the video are fed to the video encoder. At the receiver the decoded video is resized to original dimensions by predicting the skipped rows. Also analysis is also done on down sampling and up-sampling of video before and after encoder and decoder respectively. Unsharp masking is implemented and a new technique for edge boosting using DWT is proposed

Current video compression algorithms: Comparisons, optimizations, and improvements

Compression algorithms have evolved significantly in recent years. Audio, still image, and video can be compressed significantly by taking advantage of the natural redundancies that occur within them. Video compression in particular has made significant advances. MPEG-1 and MPEG-2, two of the major video compression standards, allowed video to be compressed at very low bit rates compared to the original video. The compression ratio for video that is perceptually lossless (losses can\u27t be visually perceived) can even be as high as 40 or 50 to 1 for certain videos. Videos with a small degradation in quality can be compressed at 100 to 1 or more; Although the MPEG standards provided low bit rate compression, even higher quality compression is required for efficient transmission over limited bandwidth networks, wireless networks, and broadcast mediums. Significant gains have been made over the current MPEG-2 standard in a newly developed standard called the Advanced Video Coder, also known as H.264 and MPEG-4 part 10. (Abstract shortened by UMI.)

Security of Forensic Techniques for Digital Images

Digital images are used everywhere in modern life and mostly replace traditional photographs. At the same time, due to the popularity of image editing tools, digital images can be altered, often leaving no obvious evidence. Thus, evaluating image authenticity is indispensable. Image forensic techniques are used to detect forgeries in digital images in the absence of embedded watermarks or signatures. Nevertheless, some legitimate or illegitimate image post-processing operations can affect the quality of the forensic results. Therefore, the reliability of forensic techniques needs to be investigated. The reliability is understood in this case as the robustness against image post-processing operations or the security against deliberated attacks. In this work, we first develop a general test framework, which is used to assess the effectiveness and security of image forensic techniques under common conditions. We design different evaluation metrics, image datasets, and several different image post-processing operations as a part of the framework. Secondly, we build several image forensic tools based on selected algorithms for detecting copy-move forgeries, re-sampling artifacts, and manipulations in JPEG images. The effectiveness and robustness of the tools are evaluated by using the developed test framework. Thirdly, for each selected technique, we develop several targeted attacks. The aim of targeted attacks against a forensic technique is to remove forensic evidence present in forged images. Subsequently, by using the test framework and the targeted attacks, we can thoroughly evaluate the security of the forensic technique. We show that image forensic techniques are often sensitive and can be defeated when their algorithms are publicly known. Finally, we develop new forensic techniques which achieve higher security in comparison with state-of-the-art forensic techniques

Harmonic Convolutional Networks based on Discrete Cosine Transform

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. We propose to learn these filters as combinations of preset spectral filters defined by the Discrete Cosine Transform (DCT). Our proposed DCT-based harmonic blocks replace conventional convolutional layers to produce partially or fully harmonic versions of new or existing CNN architectures. Using DCT energy compaction properties, we demonstrate how the harmonic networks can be efficiently compressed by truncating high-frequency information in harmonic blocks thanks to the redundancies in the spectral domain. We report extensive experimental validation demonstrating benefits of the introduction of harmonic blocks into state-of-the-art CNN models in image classification, object detection and semantic segmentation applications.Comment: arXiv admin note: substantial text overlap with arXiv:1812.0320

Wavelet-Packet Powered Deepfake Image Detection

As neural networks become more able to generate realistic artificial images, they have the potential to improve movies, music, video games and make the internet an even more creative and inspiring place. Yet, at the same time, the latest technology potentially enables new digital ways to lie. In response, the need for a diverse and reliable toolbox arises to identify artificial images and other content. Previous work primarily relies on pixel-space CNN or the Fourier transform. To the best of our knowledge, wavelet-based gan analysis and detection methods have been absent thus far. This paper aims to fill this gap and describes a wavelet-based approach to gan-generated image analysis and detection. We evaluate our method on FFHQ, CelebA, and LSUN source identification problems and find improved or competitive performance.Comment: Source code is available at https://github.com/gan-police/frequency-forensic

How Far Can We Get with Neural Networks Straight from JPEG?

Convolutional neural networks (CNNs) have achieved astonishing advances over the past decade, defining state-of-the-art in several computer vision tasks. CNNs are capable of learning robust representations of the data directly from the RGB pixels. However, most image data are usually available in compressed format, from which the JPEG is the most widely used due to transmission and storage purposes demanding a preliminary decoding process that have a high computational load and memory usage. For this reason, deep learning methods capable of leaning directly from the compressed domain have been gaining attention in recent years. These methods adapt typical CNNs to work on the compressed domain, but the common architectural modifications lead to an increase in computational complexity and the number of parameters. In this paper, we investigate the usage of CNNs that are designed to work directly with the DCT coefficients available in JPEG compressed images, proposing a handcrafted and data-driven techniques for reducing the computational complexity and the number of parameters for these models in order to keep their computational cost similar to their RGB baselines. We make initial ablation studies on a subset of ImageNet in order to analyse the impact of different frequency ranges, image resolution, JPEG quality and classification task difficulty on the performance of the models. Then, we evaluate the models on the complete ImageNet dataset. Our results indicate that DCT models are capable of obtaining good performance, and that it is possible to reduce the computational complexity and the number of parameters from these models while retaining a similar classification accuracy through the use of our proposed techniques.Comment: arXiv admin note: substantial text overlap with arXiv:2012.1372

Image Restoration using Automatic Damaged Regions Detection and Machine Learning-Based Inpainting Technique

In this dissertation we propose two novel image restoration schemes. The first pertains to automatic detection of damaged regions in old photographs and digital images of cracked paintings. In cases when inpainting mask generation cannot be completely automatic, our detection algorithm facilitates precise mask creation, particularly useful for images containing damage that is tedious to annotate or difficult to geometrically define. The main contribution of this dissertation is the development and utilization of a new inpainting technique, region hiding, to repair a single image by training a convolutional neural network on various transformations of that image. Region hiding is also effective in object removal tasks. Lastly, we present a segmentation system for distinguishing glands, stroma, and cells in slide images, in addition to current results, as one component of an ongoing project to aid in colon cancer prognostication

Review on passive approaches for detecting image tampering

This paper defines the presently used methods and approaches in the domain of digital image forgery detection. A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Passive, or blind, approaches for detecting image tampering are regarded as a new direction of research. In recent years, there has been significant work performed in this highly active area of research. Passive approaches do not depend on hidden data to detect image forgeries, but only utilize the statistics and/or content of the image in question to verify its genuineness. The specific types of forgery detection techniques are discussed below