After the appearance of Image Colorization in the literature and the different developments of colorization techniques, it was necessary to search for new applications for this new field rather than coloring gray images. This thesis is a research and implementation study for various applications that can exploit from colorization techniques. Three proposed colorization applications are proposed in this thesis; Automatic Movie Colorization System, Color Image Encoding System Using HSI Space Embedding System and Color Image Encoding System Using Morphological Decolorization. The first application of image colorization attracts researchers in this field is Old Movies Colorizing. This thesis presents a new proposal for a system that works on coloring the old movies automatically. The proposed system is based on coloring the film shot by shot instead of frames as it is common in this area. This is done by splitting the film into shots and coloring the first frame in the shot (the key frame) . After that the motion vector between the frames of each shot is generated to transfer the colors from the key frame to the following frames in the shot using the motion vector.
Color image encoding becomes an important application for image colorization. The idea is to remove colors from color images at the sender side while retaining the information about colors to enable image recolorization at the receiver side. The reason behind this methodology is to exploit from the smaller size of gray images. At
the receiver side, the colors are restored and the images are recolored. This Methodology of encoding is called Image Decolorization. The thesis presents two different approaches to color images decolorization: Color Embedding and Automatically Color Seeds Selection. A new system to compress the color channels in the color model "Hue, Saturation and Intensity" (HSI) is proposed. The encoded chromatic channels are hided inside the lighting channel using the “Least Significant Bit “(LSB) method. This is done by converting the Hue channel into objects and then to be encoded by an Object Compression method. For Saturation channel, there are two methods to compress S channel are proposed; "the Minimum Color Difference" (MCD) and "Y(Luma) Intensity Difference"(YID). The third proposed system is
a new automatic color seeds selection method based on Morphology . The seeds are extracted from the inner boundaries of image objects and hided in the luminance channel using LSB method.
Table of Contents
CHAPTER ONE : INTRODUCTION
1.1DIGITAL IMAGE FUNDAMENTALS
1.2 COLORING PROBLEM:
1.3 COLORIZATION TECHNIQUES
1.4 DECOLORIZATION
1.5 RESEARCH OBJECTIVES
1.6 THESIS MOTIVATIONS
1.7 THESIS ORGANIZATION:
CHAPTER TWO : LITERATURE SURVEY
2.1 COLORIZATION TECHNIQUE
2.1.1 Transformational Coloring
2.1.2 Image Matching /Coloring by Reference
2.1.3 User Selection/Colorization by Seeds
2.2 COLORIZATION APPLICATIONS
2.2.1 Movies Colorization
2.2.2 Medical Images Colorization
2.2.3 Color Image Compression
2.3 DECOLORIZATION TECHNIQUES
2.3.1 Automatic Seeds Selection
2.3.2 Color Embedding Decolorization
CHAPTER THREE : MOVIE COLORIZATION SYSTEM
3.1 INTRODUCTION
3.2 PROPOSED MOVIE COLORIZATION SYSTEM
3.2.1 Shot Cut Detection Subsystem
3.2.2 Frame Colorization Subsystem
3.2.3 Motion Deteection subsystem
3.2.4 Shot colorization subsystem
3.3 RESULTS AND DISCUSSION
3.3.1 Coloring Quality
3.4 MARKET RESEARCH
3.4.1 Processing Time
3.4.2 Market Need:
3.4.3 Market Demand:
3.4.4 Competing Products:
CHAPTER FOUR: COLOR EMBEDDING FOR HSI MODEL
4.1 INTRODUCTION
4.2 THE PROPOSED COLOR ENCODING SYSTEM
4.2.1 Hue Proposed Encoder System
4.2.2 Saturation Encoding Technique
4.2.3 Intensity Encoding
4.3 RESULTS AND DISCUSSIONS
4.3.1 Other Quality Measures
4.3.2 More System Results:
4.4 COLOR EMBEDDING
4.4.1 Experiment (1) Using MCD
4.4.2 Experiment (2) Using YID
CHAPTER FIVE: MORPHOLOGICAL DECOLORIZATION SYSTEM
5.1 INTRODUCTION
5.2 DECOLORIZATION USING MORPHOLOGY
5.3 SYSTEM QUALITY ASSESSMENTS
5.4 RESULTS AND DISCUSSIONS
5.4.1 Seeds Selection Evaluation
5.4.2 The System Compression Professionalism
5.4.3 Comparison With JPEG/JPEG2000
5.5 SEEDS HIDING
CHAPTER SIX : CONCLUSION AND FUTURE WORKS
6.1 RESEARCH CONCLUSIONS
6.2 FUTURE WORKS:
6.2.1 Movies Colorization System Future Works
6.2.2 Color Embedding For HSI Model Future Work
6.2.3 Morphological Decolorization System Future Work
Research Objectives and Themes
The thesis aims to explore and implement novel applications for image colorization techniques beyond simply adding color to grayscale images. It seeks to develop fully automated systems for colorizing old black and white movies, as well as to innovate in the field of image "Decolorization"—a method of encoding color information into grayscale images to benefit from their smaller file sizes while allowing for high-quality restoration.
- Automatic colorization of legacy black and white films.
- Advanced image decolorization for efficient data compression.
- Integration of HSI color model for improved encoding schemes.
- Morphological processing for automated color seed selection.
- Performance evaluation of proposed systems against standard JPEG/JPEG2000 benchmarks.
Extract from the Book
3.2.1 Shot Cut Detection Subsystem
Shot cut subsystem is responsible of dividing the movie in to its major shots and outputs only the key frames. 2D correlation is used to measure the similarity between shots. Correlation between each two adjacent frames FA, FB of size m×n is computed using the 2D correlation equation [18].
Where μA is the mean of FA and μB is the mean of FB. Then a threshold Corrth is used to get the threshold of each 500 frames. Corrth=min(Corr)+ k(max(Corr)-min(Corr)).
Where, k is a linear coefficient that was selected after trial and error to be 0.65 and Corr is the correlation vector of all the correlation coefficients between all the shot frames. To prevent taking a threshold for one shot frames, Corrth<0.85 was considered. Figure 3. 3 shows the shot cut results of 500 frames of the Egyptian classic movie "Ismaiel Yassen Fe El-Ostool". Figure 3. 3 (a) shows the key frames in the right column and the previous frames in the left column. Reader can notice the difference between the key frame and the previous frame (e.g.: 5612, 5611) and the similarity between the key frame till the next key frame (e.g. 5612: 5803). Figure 3. 3 (b) shows the correlation values of 500 frames. The red line represents the selected threshold to get the final shot cuts. To verify the correct cuts, the difference between the correlation coefficients is computed and another threshold is computed to get the cuts all over the difference map (Figure 3. 3 (c) ).
Summary of Chapters
CHAPTER ONE : INTRODUCTION: This chapter provides an overview of the gray image colorization problem and establishes the foundation by discussing digital image fundamentals and the motivations behind the research.
CHAPTER TWO : LITERATURE SURVEY: This chapter reviews various existing colorization and decolorization techniques found in literature and classifies them into standardized categories for analysis.
CHAPTER THREE : MOVIE COLORIZATION SYSTEM: This chapter introduces a fully automated system designed to colorize black and white movies by processing them shot-by-shot using motion estimation and key frame color transfer.
CHAPTER FOUR: COLOR EMBEDDING FOR HSI MODEL: This chapter proposes a novel decolorization and encoding system specifically designed for the HSI color model by embedding chrominance data into the intensity channel.
CHAPTER FIVE: MORPHOLOGICAL DECOLORIZATION SYSTEM: This chapter details an automated color seed selection method utilizing morphological operations to improve colorization results while maintaining high compression ratios.
CHAPTER SIX : CONCLUSION AND FUTURE WORKS: This chapter summarizes the research findings and outlines potential future improvements, such as enhancing shot cut detection and optimizing seed selection algorithms.
Keywords
Image Colorization, Decolorization, HSI Color Model, Morphological Operations, Shot Cut Detection, Motion Estimation, Image Compression, Huffman Coding, JPEG2000, Peak Signal to Noise Ratio, Mean Square Error, Structural Similarity Index, Colorfulness Metric, Mean Opinion Score.
Frequently Asked Questions
What is the core focus of this dissertation?
The work primarily focuses on advancing image colorization techniques and developing efficient color image encoding (decolorization) methods to facilitate better compression and restoration of image data.
What are the central thematic fields covered?
The research bridges the areas of digital image processing, specifically focusing on color models (like HSI), movie colorization, morphological image processing, and data compression methodologies.
What is the primary goal of the proposed movie colorization system?
The objective is to create a fully automated system that colorizes black and white movies efficiently by processing them shot-by-shot rather than frame-by-frame, significantly reducing labor and processing time.
What scientific methodology is utilized in this research?
The author employs a mix of quantitative analysis and experimental system building, including the use of 2D correlation for shot detection, motion estimation (Three Step Method), and morphological transformations to automate the selection of color seeds.
What does the main body of the thesis investigate?
The main chapters investigate three specific contributions: a movie colorization system, an HSI-based color encoding scheme using object compression, and a morphological decolorization system that extracts image seeds automatically.
Which keywords characterize this work?
The most relevant keywords include Image Colorization, Decolorization, HSI Color Model, Morphological Operations, Shot Cut Detection, and Image Compression.
How does the proposed HSI encoding system differ from standard JPEG?
Unlike JPEG, which typically applies similar encoding logic to all channels in YCbCr, this system treats each channel in the HSI model differently, applying lossless compression to the Hue channel and specialized lossy techniques to the Saturation channel to maintain high color quality.
What role do morphological operations play in the proposed decolorization system?
Morphological operations are used to automatically detect the inner boundaries and skeletons of color objects in an image, which are then used as "seeds" for the colorization process, eliminating the need for manual user input.
- Citar trabajo
- Dr. Noura Semary (Autor), 2011, Image coloring Techniques and Applications, Múnich, GRIN Verlag, https://www.grin.com/document/205750
