Bokeh is defined as a soft out of focus blur. An image with bokeh has a subject in focus and an artistically blurry background. To capture images with real bokeh, specific camera parameter choices need to be made. One essential choice is to use a big lens with a wide aperture. Because of smartphone cameras’ small size, it becomes impossible to achieve real bokeh. Commonly, new models of smartphones have artificial bokeh implemented when taking pictures, but it is uncommon to be able to capture videos with artificial bokeh. Video segmentation is more complicated than image segmentation because it puts a higher demand on performance. The result should also be temporally consistent. In this project, the aim is to create a method that can apply real-time video bokeh on a smartphone.   The project consists of two parts. The first part is to segment the subject of the video. This process is performed with convolutional neural networks. Three image segmentation networks were implemented for video, trained, and evaluated. The model that illustrated the most potential was the SINet model and was chosen as the most suitable architecture for the task.  The second part of the project is to manipulate the background to be aesthetically pleasing while at the same time mimicking real optics to some degree. This is achieved by creating a depth and contrast map. With the depth map, the background can be blurred based on depth. The shape of the bokeh shapes also varies with the depth. The contrast map is used to locate bokeh points. The main part of the project is the segmentation part.  The result for this project is a method that achieves an accurate segmentation and creates an artistic background. The different architectures illustrated similar results in terms of accuracy but different in terms of inference time. Situations existed where the segmentation failed and included too much of the background. This could potentially be counteracted with a bigger and more varied dataset. The method is performed in real-time on a computer but no conclusions could be made if it works in real-time on a smartphone.

散景是一種讓影像背景模糊化的拍攝視覺效果，這種拍攝視覺效果以往在攝影領域中都只停留在技術層次的討論，因此本研究希望去深入探討散景對於社會文化的影響，並且描繪出散景在攝影文化中的定位。 本研究採用質化深度訪談的方式，輔以文獻資料分析法，線上資料搜集整理分析，以及散景照片實驗等，透過四種類型的資料交叉對話，從散景的視覺認知及影像訊息控制開始談起，進而發掘出散景的文化意義及符號象徵，最後再探討如何透過散景的符號象徵去建構社會資本。 研究發現如下：(一) 散景透過影像的呈現來畫分出大眾攝影族群、(二) 散景造就人像鏡的神話建構、(三) 沙龍攝影文化的興衰、(四) 透過散景建構的社會資本、(五) 後製對散景的象徵符號及社會資本之解構。 本研究建議：(一)散景研究應該跳脫技術層面，拉高視野、(二) 意識到數位影像時代的散景影響層面擴大、(三) 影像文本分析應該留意容易被忽略的散景。

Depth of field is a naturally occurring effect in lenses describing the distance between theclosest and furthest object that appears in focus. The effect is commonly used in film andphotography to direct a viewers focus, give a scene more complexity, or to improve aes-thetics. In computer graphics, the same effect is possible, but since there are no naturaloccurrences of lenses in the virtual world, other ways are needed to achieve it. There aremany different approaches to simulate depth of field, but not all are suited for real-time usein computer games. In this thesis, multiple methods are explored and compared to achievedepth of field in real-time with a focus on computer games. The aspect of bokeh is alsocrucial when considering depth of field, so during the thesis, a method to simulate a bokeheffect similar to reality is explored. Three different methods based on the same approachwas implemented to research this subject, and their time and memory complexity weremeasured. A questionnaire was performed to measure the quality of the different meth-ods. The result is three similar methods, but with noticeable differences in both quality andperformance. The results give the reader an overview of different methods and directionsfor implementing it on their own, based on which requirements suits them.