Resolution Independent 2D Cartoon Video Conversion

Resolution independent 2D cartoon video conversion

CODEC

Members

114001V MAM Aazeer114003E MFHM Adheeb114039T MSF Fayaza114184G KF

Muhaiminah

Supervised byMr. Saminda Premaratne

Dr. Lochandaka Ranathunga

Overview❏ Introduction❏ Background & Motivation❏ Problem in brief❏ Aim & Objective❏ Scope ❏ Technology adapted ❏ Design❏ Implementation

Introduction❏ Most of the videos are in good quality in specific resolution and

can be clearly viewed through the specific resolution.❏ When a low quality video is played in high resolution frames are

blurred and pixelated.❏ Our aim is to come up with a solution that would overcome

resolution dependency of videos

Background & Motivation❏ High quality video is always in demand❏ Bandwidth and storage problems❏ The aim is to improve the visual appearance of the video❏ Can use video processing/Image processing technique

Problem in brief❏ When resolution of a video is increased, most of the digital videos

get blurred and pixelated

Solution❏ Convert the raster image frames to vector image frames

Aim❏ Provide an effective application to convert 2D cartoon videos

independent on resolution

Objective ❏ Learn about video processing and image processing❏ Study about the object detection, identification and its problems❏ Study about motion tracking algorithms❏ Study the problems in converting raster image to vector image ❏ Study the algorithms in scene segmentation ❏ Study about animation and transformation of images ❏ Design and develop the system to convert 2D cartoon videos to

any resolution without affecting the quality of a video.

Scope ❏ We have limited our scope to 2D cartoons. ❏ support for abrupt scene changes❏ simple user assistance needed for track the motion of foreground

objects

Technology adapted❏ OpenCV❏ C++❏ visual studio❏ SVG❏ Potrace

Design

END

List❏ The modules above will maintain and refer the following lists

throughout the program, which shall be common and available to all modules. The lists will maintain references and cross references to data items that are processed by these modules.

● Scene list● Background list● Character list● Vector background list● Vector character list● Animation List

Implementation ❏ module 1: Scene Segmentation, Background Extraction,

Background Filling , Background vectorization❏ Module 2 : Foreground Extraction and Identification, Foreground

Vectorization❏ module 3 : Object Motion Path Tracking ❏ Module 4: Reproducing Animation

Scene Segmentation, Background Extraction, Filling and

VectorizationThe main task of this module is segment the video into scene and extracting the background❏ By comparing adjacent frames of video, segment the video into

scenes❏ Extract background❏ Background filling ❏ Background vectorization.

Experiments

Scene Detection By histogram comparison

Foreground Extraction and Identification, Foreground

Vectorization The main task of this module is to discover the moving objects in a scene and identifying them as foreground objects.

❏ Classifying foreground

❏ Identifying Classified foreground

❏ Tracking foreground objects

❏ Vectorizing Objects

Experiments

Background Subtraction Using Static Background Image

Object Motion Path Detection Main task of the module is monitoring the movement of the characters and track the path of each moving character. As a result of this animation list created.❏ color of object is taken as a user input ❏ convert BGR to HSV color space values and assign minimum and

maximum HSV values❏ Threshold all the frames to the HSV color❏ filter object and track x,y coordinates

Experiment

ReproducingThe main task of this module is to reproduce the output video.❏ To do that this modules takes all the list created by other

modules and motion path of each object.❏ Place vector background on the output image❏ Place objects on the background on the relevant points.❏ The final output can be given as vector frames or reproduces as

video with raster frames scales to the needed resolution

Experiment using cv::Point data structure

ConclusionWe have been successful in vectorizing raster

images by being able to find a novel approach to vectorizing color images using k-means clustering and potrace

We have introduced a novel approach to vectorizing 2D cartoon videos by vectorizing the foreground and background separately and by using these vectorized components to re produce the video

Further workAs further work, we hope to automate color

selection for tracking and number of clusterin for vectorizing images.

We also will publish a web based tool to vectorize color images

Questions?