Course Objectives
(1) Understand the scope and examples of applications of image encoding technology.
(2) Understand the nature of image information and understand why image encoding technology is needed.
(3) Understand the outline and characteristics of various types of image encoding.
(4) Understand the practical use of basic image processing and image encoding technologies.
Rubric
| Ideal Level | Standard Level | Unacceptable Level |
| Achievement 1 | Can fully explain the scope and examples of applications of image encoding technologies. | Can explain the scope and examples of applications of image encoding technologies. | Cannot explain the scope and examples of applications of image encoding technologies. |
| Achievement 2 | Understand the nature of image information and can accurately explain why image encoding technologies are needed . | Understand the nature of image information and can explain why image encoding technologies are needed. | Cannot explain the nature of image information and why image encoding technologies are needed. |
| Achievement 3 | Can specifically explain the outline and characteristics of various types of image encoding. | Can explain the outline and characteristics of various types of image encoding. | Cannot explain the outline and characteristics of various types of image encoding. |
| Can accurately explain the practical use of basic image processing and image encoding technologies | Can explain the practical use of basic image processing and image encoding technologies. | Cannot explain the practical use of basic image processing and image encoding technologies. |
Assigned Department Objectives
Teaching Method
Outline:
When handling images as digital information, technologies for reducing their data volume (image encoding or image compression) are a must. In this lecture, we will be explaining the nature of the image information briefly, and then giving lectures on various image encoding technologies. In addition, we will ensure the knowledge learned in the lecture by doing exercise assignments using matrix computing software, etc.
Style:
Slides will be mainly used to explain the content in class. Also, since this is a learning-credit subject, there will be three to four assignments over the course of half a semester. Assignments will be about creating programs that perform specified processes, so we will explain the application students can used for the assignments in advance.
Notice:
This course's content will amount to 90 hours of study in total. These hours include the learning time guaranteed in classes and the standard self-study time required for pre-study / review, and completing assignment reports. As this is a learning-credit subject, there will be three to four assignments over the course of half a semester. All assignments must be submitted to earn the credits. Since the assignments involve programming, it's desirable to have experiences in programming (in any language).
Students who miss 1/3 or more of classes will not be eligible for a passing grade.
Characteristics of Class / Division in Learning
Course Plan
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|
|
Theme |
Goals |
| 2nd Semester |
| 3rd Quarter |
| 1st |
The nature of image information
|
Digitized image information is generally said to have stronger image correlation. Can explain what image correlation is and what happens when image correlation is stronger.
|
| 2nd |
Image manipulation by Python (1)
|
Understand how to use Python to accomplish the assignments.
|
| 3rd |
Image manipulation by Python (2) |
Can use Python to do the processing given as an assignment.
|
| 4th |
Entropy encoding (1)
|
Can explain the concept of entropy encoding, which is often used together with various types of encoding.
|
| 5th |
Entropy encoding (2)
|
Can briefly explain Huffman and arithmetic encodings as typical techniques for entropy encoding.
|
| 6th |
Predictive encoding (1)
|
Can explain the principle of predictive encoding, the simplest of image encoding.
|
| 7th |
Predictive encoding (2)
|
Can explain the characteristics of predictive encoding, and can explain how to compensate for the shortcomings.
|
| 8th |
Midterm exam
|
|
| 4th Quarter |
| 9th |
Transform encoding (1)
|
Can explain the concept of transform encoding, and can explain the two-dimensional discrete cosine transform (DCT), which is the mainstream of image encoding today.
|
| 10th |
Transform encoding (2)
|
Can explain JPEG, which is an image coding method based on DCT.
|
| 11th |
Wavelet transformation
|
Can briefly explain the wavelet transform, which is gaining attention as the next-generation method of transform encoding.
|
| 12th |
Vector quantization (1)
|
Can explain the overview of vector quantization, an extension of scalar quantization.
|
| 13th |
Vector quantization (2)
|
Can explain the performance, design techniques and challenges of vector quantization.
|
| 14th |
Other image encoding
|
Can explain outline of other image encoding methods such as block truncation encoding, progressive encoding, etc.
|
| 15th |
Video encoding
|
Can explain various video encoding methods briefly.
|
| 16th |
Final exam
|
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Evaluation Method and Weight (%)
| Examination | Presentation | Mutual Evaluations between students | Behavior | Exercise | Other | Total |
| Subtotal | 70 | 0 | 0 | 0 | 30 | 0 | 100 |
| Basic Proficiency | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Specialized Proficiency | 70 | 0 | 0 | 0 | 30 | 0 | 100 |
| Cross Area Proficiency | 0 | 0 | 0 | 0 | 0 | 0 | 0 |