Distortion Sensitive Algorithm to Preserve Line Structure Properties in Image Resampling

In order to remove less important content from image seam carving algorithm is used. In seam carving distortion is very low as compared to other techniques like scaling and cropping. The major drawback of seam carving is when seam intersects with straight line present in the image it distorts line structure; the line may become curve after distortion. This structure distortion not only degrades visual quality of image but also gives artifacts or aliased line structure. This paper presents a content aware seam carving algorithm to resize the image. After applying algorithm discussed the structure of regular objects present in the image can be preserved. In the proposed algorithm first line detection algorithm is applied over the image in order to detect possible straight lines present in the image. After detecting straight lines algorithm tries to find out intersection point of optimal seam with the straight line. Algorithm increases energy of local neighbourhood pixels of intersection point up to a predefined radius, so that no further seam can intersect same pixel again

Panoramic Image Communication for Mobile Application using Content-Aware Image Resizing Method

This paper presents an image resizing application for mobile communication to evaluate content-aware image resizing method for panoramic image. In many applications, we can take account into aspect ratio changing, removal or pan and zoom in the image. However, the implemented application in this work is more focus on image downsizing due to mobile application that is limited for image capacity. The generated panoramic image will be distorted if simply scaling by factors and the image will lose information or generate artifacts if crop the area directly. It is meaningful to discuss how to keep the main object in the image and resize the image by cutting off the unnecessary part. The implemented approach has been successfully developed and it will be valuable to compare image resizing on mobile terminal

Panoramic Image Communication for Mobile Application using Content-Aware Image Resizing Method

This paper presents an image resizing application for mobile communication to evaluate content-aware image resizing method for panoramic image. In many applications, we can take account into aspect ratio changing, removal or pan and zoom in the image. However, the implemented application in this work is more focus on image downsizing due to mobile application that is limited for image capacity. The generated panoramic image will be distorted if simply scaling by factors and the image will lose information or generate artifacts if crop the area directly. It is meaningful to discuss how to keep the main object in the image and resize the image by cutting off the unnecessary part. The implemented approach has been successfully developed and it will be valuable to compare image resizing on mobile terminal

Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions

Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datasets are unavailable for training deep learning models in the image retargeting tasks. As a result, we present a new supervised approach for training deep learning models. We use the original images as ground truth and create inputs for the model by resizing and cropping the original images. A second challenge is generating different image sizes in inference time. However, regular convolutional neural networks cannot generate images of different sizes than the input image. To address this issue, we introduced a new method for supervised learning. In our approach, a mask is generated to show the desired size and location of the object. Then the mask and the input image are fed to the network. Comparing image retargeting methods and our proposed method demonstrates the model's ability to produce high-quality retargeted images. Afterward, we compute the image quality assessment score for each output image based on different techniques and illustrate the effectiveness of our approach.Comment: 18 pages, 5 figure

Improved content aware scene retargeting for retinitis pigmentosa patients

<p>Abstract</p> <p>Background</p> <p>In this paper we present a novel scene retargeting technique to reduce the visual scene while maintaining the size of the key features. The algorithm is scalable to implementation onto portable devices, and thus, has potential for augmented reality systems to provide visual support for those with tunnel vision. We therefore test the efficacy of our algorithm on shrinking the visual scene into the remaining field of view for those patients.</p> <p>Methods</p> <p>Simple spatial compression of visual scenes makes objects appear further away. We have therefore developed an algorithm which removes low importance information, maintaining the size of the significant features. Previous approaches in this field have included <it>seam carving</it>, which removes low importance seams from the scene, and <it>shrinkability </it>which dynamically shrinks the scene according to a generated importance map. The former method causes significant artifacts and the latter is inefficient. In this work we have developed a new algorithm, combining the best aspects of both these two previous methods. In particular, our approach is to generate a <it>shrinkability </it>importance map using as seam based approach. We then use it to dynamically shrink the scene in similar fashion to the <it>shrinkability </it>method. Importantly, we have implemented it so that it can be used in real time without prior knowledge of future frames.</p> <p>Results</p> <p>We have evaluated and compared our algorithm to the <it>seam carving </it>and image <it>shrinkability </it>approaches from a content preservation perspective and a compression quality perspective. Also our technique has been evaluated and tested on a trial included 20 participants with simulated tunnel vision. Results show the robustness of our method at reducing scenes up to 50% with minimal distortion. We also demonstrate efficacy in its use for those with simulated tunnel vision of 22 degrees of field of view or less.</p> <p>Conclusions</p> <p>Our approach allows us to perform content aware video resizing in real time using only information from previous frames to avoid jitter. Also our method has a great benefit over the ordinary resizing method and even over other image retargeting methods. We show that the benefit derived from this algorithm is significant to patients with fields of view 20° or less.</p

Preserving Trustworthiness and Confidentiality for Online Multimedia

Technology advancements in areas of mobile computing, social networks, and cloud computing have rapidly changed the way we communicate and interact. The wide adoption of media-oriented mobile devices such as smartphones and tablets enables people to capture information in various media formats, and offers them a rich platform for media consumption. The proliferation of online services and social networks makes it possible to store personal multimedia collection online and share them with family and friends anytime anywhere. Considering the increasing impact of digital multimedia and the trend of cloud computing, this dissertation explores the problem of how to evaluate trustworthiness and preserve confidentiality of online multimedia data. The dissertation consists of two parts. The first part examines the problem of evaluating trustworthiness of multimedia data distributed online. Given the digital nature of multimedia data, editing and tampering of the multimedia content becomes very easy. Therefore, it is important to analyze and reveal the processing history of a multimedia document in order to evaluate its trustworthiness. We propose a new forensic technique called ``Forensic Hash", which draws synergy between two related research areas of image hashing and non-reference multimedia forensics. A forensic hash is a compact signature capturing important information from the original multimedia document to assist forensic analysis and reveal processing history of a multimedia document under question. Our proposed technique is shown to have the advantage of being compact and offering efficient and accurate analysis to forensic questions that cannot be easily answered by convention forensic techniques. The answers that we obtain from the forensic hash provide valuable information on the trustworthiness of online multimedia data. The second part of this dissertation addresses the confidentiality issue of multimedia data stored with online services. The emerging cloud computing paradigm makes it attractive to store private multimedia data online for easy access and sharing. However, the potential of cloud services cannot be fully reached unless the issue of how to preserve confidentiality of sensitive data stored in the cloud is addressed. In this dissertation, we explore techniques that enable confidentiality-preserving search of encrypted multimedia, which can play a critical role in secure online multimedia services. Techniques from image processing, information retrieval, and cryptography are jointly and strategically applied to allow efficient rank-ordered search over encrypted multimedia database and at the same time preserve data confidentiality against malicious intruders and service providers. We demonstrate high efficiency and accuracy of the proposed techniques and provide a quantitative comparative study with conventional techniques based on heavy-weight cryptography primitives

Adaptation of Images and Videos for Different Screen Sizes

With the increasing popularity of smartphones and similar mobile devices, the demand for media to consume on the go rises. As most images and videos today are captured with HD or even higher resolutions, there is a need to adapt them in a content-aware fashion before they can be watched comfortably on screens with small sizes and varying aspect ratios. This process is called retargeting. Most distortions during this process are caused by a change of the aspect ratio. Thus, retargeting mainly focuses on adapting the aspect ratio of a video while the rest can be scaled uniformly. The main objective of this dissertation is to contribute to the modern image and video retargeting, especially regarding the potential of the seam carving operator. There are still unsolved problems in this research field that should be addressed in order to improve the quality of the results or speed up the performance of the retargeting process. This dissertation presents novel algorithms that are able to retarget images, videos and stereoscopic videos while dealing with problems like the preservation of straight lines or the reduction of the required memory space and computation time. Additionally, a GPU implementation is used to achieve the retargeting of videos in real-time. Furthermore, an enhancement of face detection is presented which is able to distinguish between faces that are important for the retargeting and faces that are not. Results show that the developed techniques are suitable for the desired scenarios

Forensic research on detecting seam carving in digital images

Digital images have been playing an important role in our daily life for the last several decades. Naturally, image editing technologies have been tremendously developed due to the increasing demands. As a result, digital images can be easily manipulated on a personal computer or even a cellphone for many purposes nowadays, so that the authenticity of digital images becomes an important issue. In this dissertation research, four machine learning based forensic methods are presented to detect one of the popular image editing techniques, called ‘seam carving’. To reveal seam carving applied to uncompressed images from the perspective of energy distribution change, an energy based statistical model is proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A support vector machine (SVM) based classifier is employed to determine whether an image has been seam carved or not. In the second work, an advanced feature model is presented for seam carving detection by investigating the statistical variation among neighboring pixels. Comprised with three types of statistical features, i.e., LDP features, Markov features, and SPAM features, the powerful feature model significantly improved the state-of-the-art accuracy in detecting low carving rate seam carving. After the feature selection by utilizing SVM based recursive feature elimination (SVM-RFE), with a small amount of features selected from the proposed model the overall performance is further improved. Combining above mentioned two works, a hybrid feature model is then proposed as the third work to further boost the accuracy in detecting seam carving at low carving rate. The proposed model consists of two sets of features, which capture energy change and neighboring relationship variation respectively, achieves remarkable performance on revealing seam carving, especially low carving rate seam carving, in digital images. Besides these three hand crafted feature models, a deep convolutional neural network is designed for seam carving detection. It is the first work that successfully utilizes deep learning technology to solve this forensic problem. The experimental works demonstrate their much more improved performance in the cases where the amount of seam carving is not serious. Although these four pieces of work move the seam carving detection ahead substantially, future research works with more advanced statistical model or deep neural network along this line are expected