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