Dynamic load balancing in image retargeting using pipeline architecture

In today’s smart world demand of efficient multimedia based communication has increased at a rapid rate. Diversity on display sizes of gadgets used for multimedia communication confines the quality of images. Image retargeting is used as the focal solution to this problem which results in images with appropriate sizes. Enormously mounting demand of image retargeting expedites the rate of increment in computational load. This research paper expatiate and experiments a dynamic load balancing based three phase image retargeting methodology using pipeline architecture. In the first phase of image retargeting resize operation is performed on input image which results in multiple sized image copies of the same image. In the second phase resized images undergo quantization operation. In the final phase lossless compression is performed to have an expedient image. In the proposed exhibit think, we have done statistical analysis of results obtained, to confirm an impartial dynamic load balancing with a better degree of underlying resource utilization. We extend the approach to achieve significant storage optimization using three phase image retargeting

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

Sayısal görüntülerde piksel yolu çıkarma esaslı boyut değişikliği tespiti

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Piksel yolu çıkarma (seam carving), günümüzde en çok uygulanan içeriğe duyarlı görüntü boyutlandırma yöntemlerinden biridir. Piksel yolu çıkarmanın sebep olduğu bozukluklar çok yüksek oranlarda ölçekleme yapılmadıkça insan gözü tarafından algılanamaz. Bu görsel başarının sebebi görüntüdeki piksellerin önem değerlerine göre değerlendiriliyor olmasıdır. Görüntünün optimal seam'i, görüntü genelinde toplamda en az enerji (önem) değerine sahip piksel yoludur. Tek piksel genişliğindeki önemsiz bu piksel yolları birer azaltılarak her iterasyonda görüntünün genişliği ya da yüksekliği bir azaltılır. Anlamsal olarak önemli olan ön plan nesnelerine mümkün olduğunca dokunulmaz. Görüntünün içeriğinin bu denli korunduğu bir ölçekleme yaklaşımı kötü niyetli olarak da kullanılabileceğinden, bu şekilde ölçeklenmiş görüntülerin tespiti büyük önem arz etmektedir. Piksel yolu çıkarma tabanlı ölçeklemenin tespiti diğer ölçekleme yöntemlerine göre oldukça zordur. çünkü görüntülerin geometrik açıdan ele alınması yetmez, anlamsal bir değerlendirme içeren detaylı bir analiz yapılması gerekmektedir. Bu çalışmada, piksel yolu çıkarılarak boyutları değiştirilmiş görüntülerin tespiti, görüntülerden özellik çıkarılması ve çıkarılan özelliklerle Destek Vektör Makinesi'nin eğitilmesi şeklinde gerçekleştirilmektedir. Çıkarılan özellikler piksel yolu çıkarma algoritmasının uygulanışı ile alakalı özelliklerdir. Ayrıca, yöntemin başarımını artırmak amacıyla, özellik çıkarımı öncesinde görüntülere Yerel İkili Örüntüler dönüşümü uygulanmış ve piksel yolu çıkarmanın sebep olabileceği yerel bozukluklar belirginleştirilmiştir. Tüm bunlara ek olarak, piksel yolu çıkarmanın görüntülerin farklı parçalarındaki etkileri de incelenmiştir. Bu amaçla görüntüler şeritlere ayrılarak her bir şerit seam özellikleri bakımından değerlendirilmiş ve tespit doğrulukları bu şekilde oldukça artırılmıştır. Geliştirilen yöntem ile piksel yolu çıkarma tabanlı ölçekleme %30 ölçeklenmiş görüntülerde %99,9'lara kadar tespit edilebilmiştir. Performans literatürdeki diğer yöntemlere göre ortalamada %20'den fazla artırılmıştır. Tespit performansı özellikle tespit edilmesi daha zor olan %3, %6 gibi küçük ölçekleme oranlarında %26 geliştirilmiştir.Seam carving is one of the mostly applied content-aware image resizing methods today. The deteriorations caused by seam carving are mostly unnoticeable for human eyes unless the scaling ratio is very high. The reason of this visual success comes from evaluating the pixels according to their importance values. Optimal seam of an image is a pixel path which contains the least energy (importance) throughout the image. Image width or height is decreased by one in each iteration by removing those unimportant, one-pixel width pixel paths. The semantically important foreground objects remain untouched as far as possible. Since such a scaling approach which perfectly preserves the image content can be used malevolently, the detection of the images that are scaled in this manner becomes more of an issue. The detection of seam carving is more difficult than the other scaling methods since evaluating the images geometrically is not sufficient, but a detailed analysis investigating the semantical concept is required. In this study, the detection of the images scaled by seam carving is realized by feature extraction and training a Support Vector Machine with those features. The extracted features are related to the seam carving process. In addition, Local Binary Patterns transform is applied to the images before feature extraction to reveal the local artifacts caused by seam carving. Besides, the effect of seam carving in sub parts of the images is investigated. For this purpose, the images are divided into several stripes and each and every stripe is evaluated in terms of seam features. This evaluation has been improved the detection accuracies. Seam carving based resizing has been detected up to 99,9% in 30%scaled images by the developed method. The detection performance has been improved 20% on the average when compared with other methods in the literature. The detection performance is improved 26% in low scaling ratios like 3% and 6% which are harder to detect

Light field image processing: an overview

Light field imaging has emerged as a technology allowing to capture richer visual information from our world. As opposed to traditional photography, which captures a 2D projection of the light in the scene integrating the angular domain, light fields collect radiance from rays in all directions, demultiplexing the angular information lost in conventional photography. On the one hand, this higher dimensional representation of visual data offers powerful capabilities for scene understanding, and substantially improves the performance of traditional computer vision problems such as depth sensing, post-capture refocusing, segmentation, video stabilization, material classification, etc. On the other hand, the high-dimensionality of light fields also brings up new challenges in terms of data capture, data compression, content editing, and display. Taking these two elements together, research in light field image processing has become increasingly popular in the computer vision, computer graphics, and signal processing communities. In this paper, we present a comprehensive overview and discussion of research in this field over the past 20 years. We focus on all aspects of light field image processing, including basic light field representation and theory, acquisition, super-resolution, depth estimation, compression, editing, processing algorithms for light field display, and computer vision applications of light field data