💻 COMPUTISM LAB CLASS 7 — COMPLETE SOLUTION

Section A — Fill in the Blanks

1. SMPS stands for Switched-Mode Power Supply.
2. A modem converts digital data into analog signals.
3. The main circuit board of a computer system is called the motherboard.
4. RAM stands for Random Access Memory.
5. The octal number system consists of 8 digits with the base 8.
6. The hexadecimal number system includes digits from 0 to 9 and letters from A to F.
7. The term VIRUS stands for Vital Information Resources Under Siege.
8. A sector virus infects the boot sector of a hard drive.
9. The tag used to insert an image in an HTML web page is <img>.
10. Every HTML form uses a <form> element which begins the form structure.

---

Section B — Multiple Choice Questions

The paper contains 20 MCQs covering hardware, number systems, viruses and HTML.

No.	Correct Answer
1	a. Central Processing Unit
2	c. RAM
3	b. Scanner
4	c. Output
5	a. Impact printer
6	c. 16
7	b. 1010
8	a. 4D2
9	c. 2
10	b. 12
11	b. Virus
12	b. Sweeper
13	c. Worm
14	b. <tr>
15	a. Choose multiple options
16	c. <caption>
17	a. border-collapse: collapse;
18	b. src
19	b. <ol>
20	b. <a> (Anchor tag)

Important explanations

Q7. Decimal 10 → Binary

10 ÷ 2 = 5 remainder 0
5 ÷ 2 = 2 remainder 1
2 ÷ 2 = 1 remainder 0
1 ÷ 2 = 0 remainder 1

Reading from bottom to top:

10₁₀ = 1010₂

Q10. Hexadecimal C

Hexadecimal values are:

A = 10
B = 11
C = 12
D = 13
E = 14
F = 15

---

Section C — Assertion-Based MCQs

There are 10 assertion/reason questions in this section.

1. RAM

Assertion: RAM is known as a volatile memory.
Reason: Information stored in RAM is lost when you turn off the computer or power goes off.

✅ Answer: a. Both A and R are true, and R is the correct explanation of A.

---

2. ROM

Assertion: ROM is a non-volatile memory.
Reason: ROM retains stored information even when the power has been switched off.

✅ Answer: a. Both A and R are true, and R is the correct explanation of A.

---

3. Binary Number System

Assertion: The binary number system uses base 2.
Reason: Binary number system consists of ten digits from 0 to 9.

✅ Answer: b. A is true, but R is false.

Explanation: Binary uses only 0 and 1.

---

4. Virus and Flash Drive

Assertion: A computer virus can spread via removable storage devices like flash drives.
Reason: Flash drives can carry malicious files and execute AutoRun code to compromise host machines.

✅ Answer: a. Both A and R are true, and R is the correct explanation of A.

---

5. Antivirus Updates

Assertion: Antivirus software updates its virus database frequently.
Reason: Frequent updates ensure the system can detect new and different types of virus threats effectively.

✅ Answer: a. Both A and R are true, and R is the correct explanation of A.

---

6. Computer Worm

Assertion: A computer worm is a self-replicating program that can travel without human action.
Reason: Unlike viruses, worms do not consume network bandwidth or memory.

✅ Answer: b. A is true, but R is false.

Explanation: Worms can consume network bandwidth, memory and other system resources.

---

7. <img> Tag

Assertion: The <img> tag requires a closing tag like </img> in HTML.
Reason: The <img> tag is an empty tag used to add images to a web page.

✅ Answer: c. A is false, but R is true.

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8. HTML Form

Assertion: The action attribute in an HTML form specifies the file or script the form is submitted to.
Reason: The method attribute specifies how the form information is posted (GET or POST).

✅ Answer: a. Both A and R are true, and R is related to form submission settings.

---

9. Octal Number System

Assertion: Octal number system consists of 8 digits with the base 8.
Reason: Octal system takes binary digits in groups of three to make binary more compact.

✅ Answer: a. Both A and R are true, and R explains the reason behind the octal system.

---

10. Real-Time Antivirus Scan

Assertion: Real-time scan in antivirus software runs continuously in the background.
Reason: Real-time scan provides maximum protection by screening opened or saved files for security risks.

✅ Answer: a. Both A and R are true, and R is the correct explanation of A.

---

Section D — True or False

The following are the 10 statements given in the paper.

No.	Statement	Answer
1	RAM stores information permanently.	❌ False
2	A digital camera is classified as an output device.	❌ False
3	Binary numbers use digits from 0 to 7.	❌ False
4	The binary system is the most commonly used number system by digital computers.	✅ True
5	Data is an important asset for an organisation or individual.	✅ True
6	Viruses cannot be sent as executable files (.exe) with email attachments.	❌ False
7	A computer worm can travel without any human action.	✅ True
8	The <img> tag must always be closed with </img>.	❌ False
9	External links lead to a different website or web page.	✅ True
10	The border-spacing property is used to change the distance between table cells.	✅ True

COMPUTISM LAB CLASS 6 — COMPLETE SOLUTION

 COMPUTISM LAB CLASS 6 — COMPLETE SOLUTION

Section A — Fill in the Blanks

1. A table is an organised arrangement of text in the form of rows and columns.
2. The intersection of a column and a row in a table is called a cell.
3. A small hollow square at the bottom-right corner of a table is called the Resize Handle.
4. Combining two or more adjacent cells horizontally or vertically to make a single cell is called Merging Cells.
5. Dividing a selected cell into multiple separate cells is known as Splitting Cells.
6. The Mail Merge feature in Microsoft Word helps you create personalised letters for people at the same time.
7. Each row in a Mail Merge data source table is called a record.
8. In a data source, each column is referred to as a field.
9. The <b> tag is used to display bold text in HTML.
10. A group of related web pages linked together is called a website.

---

Section B — Multiple Choice Questions

No.	Answer	Correct Option
1	My Table	c
2	Insert Table	b
3	AutoFit Contents	b
4	Merge Cells	c
5	Tab Key	b
6	Table Move Handle	a
7	Data Source	b
8	Main Document	c
9	Merge Fields	a
10	Mailings	c
11	HyperText Markup Language	b
12	<h1>	c
13	<br>	c
14	bgcolor	b
15	Not case-sensitive	b
16	<br>	b
17	<i>	c
18	<html>	b
19	Borders and Shading	a
20	Snap Assist	b

These MCQs correspond to the questions published on the examination page.

---

Section C — Assertion-Based MCQs

1.

Assertion: A table makes it easier to organise and present data cleanly in rows and columns.

Reason: The intersection of a row and a column is called a cell, which holds individual data items.

✅ Answer: a. Both (A) and (R) are true and (R) is the correct explanation of (A).

2.

Assertion: Mail Merge combines a single master document with a recipient list.

Reason: It saves time by generating personalised letters for multiple people simultaneously.

✅ Answer: b. Both (A) and (R) are true but (R) is not the correct explanation of (A).

3.

Assertion: HTML tags are case-sensitive and must always be written in strict uppercase.

Reason: Standard web browsers do not care about casing and treat lowercase and uppercase HTML tags identically.

✅ Answer: c. (A) is false, but (R) is true.

4.

Assertion: The <br> tag is an example of an empty element in HTML.

Reason: Empty elements do not require a separate closing tag to function correctly.

✅ Answer: a. Both (A) and (R) are true and (R) is the correct explanation of (A).

5.

Assertion: Data sources in Mail Merge arrange recipient records in a structured table format.

Reason: Each column in a data source table represents a distinct record, and rows represent fields.

✅ Answer: b. (A) is true, but (R) is false.

Correct concept: Rows are records and columns are fields.

6.

Assertion: You can manually resize a Word table using its Table Resize Handle.

Reason: The Table Resize Handle is located at the top-left corner of the table.

✅ Answer: b. (A) is true, but (R) is false.

The resize handle is at the bottom-right corner.

7.

Assertion: Merge fields act as placeholders in the main document during a Mail Merge operation.

Reason: Word replaces these placeholders with actual recipient information during the merge process.

✅ Answer: a. Both (A) and (R) are true and (R) is the correct explanation of (A).

8.

Assertion: The <title> tag in an HTML document usually appears inside the <head> section.

Reason: The head section contains metadata and information about the web page that is not directly displayed in the main browser body.

✅ Answer: a. Both (A) and (R) are true and (R) is the correct explanation of (A).

9.

Assertion: Splitting a cell combines multiple cells into one unified cell space.

Reason: The split cell command divides an existing single cell into multiple rows and columns.

✅ Answer: c. (A) is false, but (R) is true.

10.

Assertion: Web pages are created using specialised coding languages like HTML.

Reason: HTML uses predefined tags to structure text, images, and links for web browsers.

✅ Answer: a. Both (A) and (R) are true and (R) is the correct explanation of (A).

---

Section D — True or False

No.	Statement	Answer
1	Once a table is created in Microsoft Word, it is impossible to delete or remove it.	❌ False
2	AutoFit automatically adjusts column widths based on contents or window dimensions.	✅ True
3	Each individual row in a Mail Merge data source is known as a record.	✅ True
4	The <br> tag is a container tag requiring a matching end tag.	❌ False
5	The <font> tag can modify text properties such as size and colour in HTML.	✅ True
6	The Table Move handle has a small cross inside a square at the top-left corner.	✅ True
7	Notepad is a WYSIWYG web design editor.	❌ False
8	Merging cells combines two or more adjacent cells into one larger cell.	✅ True
9	The <u> tag is used in HTML to underline text.	✅ True
10	New columns can be inserted either to the left or right of an existing column.	✅ True

2-D DCT Value Generator

2-D DCT Value Generator

Generate a random 8 × 8 matrix and calculate only its DCT values step by step

📐 DCT Formula

D(u,v) = 1/4 × C(u) × C(v) × Σ Σ f(x,y) cos[(2x+1)uπ/16] cos[(2y+1)vπ/16]

C(0) = 1/√2     C(k) = 1 for k > 0

Step 1 — Random 8 × 8 Matrix

Step 2 — DCT Calculation

Click ▶ NEXT DCT VALUE to calculate the DCT coefficients one by one.

DCT Values Calculated: 0 / 64

Step 3 — DCT Values

© B.K.PAUL • DCT Educational Visualization

🔐 Lossless Image Compression

Interactive Image Processing Lab • PNG • RLE • Huffman • LZW

📚 What is Lossless Compression?

Lossless compression reduces the size of an image without permanently removing image information.

✅ After decompression, the original image can be reconstructed exactly.

Original Image → Lossless Encoder → Compressed Data → Lossless Decoder → Original Image

📤 1. Upload an Image

Upload an image to analyse its dimensions, raw grayscale size and PNG lossless representation.

🖼️ 2. Original and Grayscale Image

Original Image

8-bit Grayscale Image

📐 3. Grayscale Image Size

0 Width

0 Height

0 Total Pixels

8 bits Bits / Pixel

Parameter	Value
Dimensions	-
Total Pixels	-
Raw Grayscale Size	-
PNG Lossless Size	-

🧮 Mathematical Calculation

📊 4. Lossless Compression Result

- Original Size

- PNG Size

- Compression Ratio

- Size Reduction

Measurement	Result
Original File	-
Lossless PNG	-
Compression Ratio	-
Size Reduction	-
Pixel Verification	-

⬇️ Download Lossless PNG

🔍 5. Lossless Pixel Verification

In lossless compression, decompression must reproduce the same pixel values.

Original Pixel Value = Reconstructed Pixel Value

Therefore:
Error = 0

MSE = 0

🔢 6. Run Length Encoding — RLE

RLE replaces consecutive repeated values with a value and its repetition count.

Original: AAAAABBBCCAAAA

RLE: A5 B3 C2 A4

🌳 7. Huffman Coding

Huffman coding assigns shorter binary codes to frequently occurring symbols and longer codes to less frequent symbols.

High Frequency Symbol → Short Code

Low Frequency Symbol → Long Code

Symbol	Frequency	Example Code
A	50%	0
B	25%	10
C	15%	110
D	10%	111

📖 8. LZW Compression

LZW is a dictionary-based lossless compression technique. Repeated patterns are represented by dictionary codes.

Input Pattern → Dictionary Search → Dictionary Code → Compressed Data

🔄 9. Lossless Image Compression Pipeline

Image

→

Pixel Data

→

Redundancy

→

RLE / LZW

→

Huffman

→

Compressed

🔎 10. Redundancy in Images

Redundancy	Meaning	Technique
Coding Redundancy	Inefficient representation of symbols	Huffman Coding
Spatial Redundancy	Neighbouring pixels are similar	RLE / Prediction
Statistical Redundancy	Some values occur more frequently	Entropy Coding

⚖️ 11. Lossless vs Lossy

Feature	Lossless	Lossy
Information Loss	❌ No	✅ Yes
Exact Recovery	✅ Yes	❌ No
Compression	Moderate	High
Quality	Original	May decrease
Examples	PNG, GIF, RLE, LZW	JPEG

💡 12. Applications

• Medical images
• Technical drawings
• Satellite imagery
• Documents and scanned images
• Graphics and logos
• Archiving
• Scientific image storage

Created by Bijan Krishna Paul
B.K.PAUL Bhairab Ganguly College

🖼️ Lossy Image Compression

Interactive JPEG Compression • DCT • Quantization • MSE • PSNR

📚 What is Lossy Compression?

Lossy compression reduces the size of an image by permanently removing some information that is less important to human visual perception.

Key idea: A smaller amount of data is stored, but the reconstructed image may not be exactly identical to the original image.

Original Image → Transformation → Quantization → Encoding → Compressed Image

📤 1. Upload Image

Upload a photograph or other image to demonstrate lossy JPEG compression.

🖼️ 2. Original vs Compressed Image

Original Image

Compressed JPEG

🎚️ 3. JPEG Quality Control

Move the slider to change JPEG compression quality.

80%

Higher quality → larger file → less information loss
Lower quality → smaller file → more information loss

📊 4. Compression Results

- Original Size

- Compressed Size

- Compression Ratio

- Size Reduction

- MSE

- PSNR

Parameter	Result
Image Dimensions	-
JPEG Quality	-
Original File Size	-
Compressed File Size	-
Compression Ratio	-
Size Reduction	-
MSE	-
PSNR	-

⬇️ Download Compressed JPEG

🧮 5. Mathematics of Lossy Compression

Step 1 — DCT

F(u,v) = 1/4 C(u)C(v) Σ Σ f(x,y) cos[(2x+1)uπ/16] cos[(2y+1)vπ/16]

DCT transforms pixel information from the spatial domain into the frequency domain.

Step 2 — Quantization

Q(u,v) = round(F(u,v) / T(u,v))

This is the main lossy operation in JPEG.

Step 3 — Encoding

Quantized Coefficients → Zig-Zag Scan → Run Length Encoding → Huffman Coding

🎯 6. Quantization Example

Suppose a DCT coefficient is:

F(u,v) = 137

T(u,v) = 10

Q(u,v) = round(137 / 10)

Q(u,v) = round(13.7)

Q(u,v) = 14

During reconstruction:

F'(u,v) ≈ 14 × 10

F'(u,v) ≈ 140

Original coefficient = 137
Reconstructed coefficient ≈ 140

Therefore, some information has been lost.

📏 7. Measuring Image Quality

Mean Squared Error — MSE

MSE = (1/N) Σ [I(x,y) − K(x,y)]²

Lower MSE means the compressed image is closer to the original image.

Peak Signal-to-Noise Ratio — PSNR

PSNR = 10 log₁₀(MAX² / MSE)

For an 8-bit image:

MAX = 255

Higher PSNR generally means better reconstructed image quality.

🔄 8. JPEG Lossy Compression Pipeline

RGB Image

→

YCbCr

→

8×8 Blocks

→

DCT

→

Quantization

→

Zig-Zag

→

RLE

→

Huffman

→

JPEG

⚖️ 9. Lossless vs Lossy Compression

Feature	Lossless	Lossy
Information Loss	No	Yes
Original Recovery	Exact	Not exact
File Size	Moderate	Usually smaller
Quality	Original quality	Depends on compression
Examples	PNG, RLE, Huffman	JPEG

✅ 10. Advantages of Lossy Compression

• Significantly reduces image file size.
• Reduces storage requirements.
• Reduces network transmission time.
• Useful for websites and online applications.
• JPEG provides adjustable quality levels.

⚠️ Disadvantages

• Some information is permanently removed.
• Repeated compression can reduce quality.
• Very high compression can produce visible artifacts.
• The original image cannot be reconstructed exactly.

Created by Bijan Krishna Paul
B.K.PAUL Bhairab Ganguly College

🖼️ Image Compression Techniques

Learn how digital images are compressed using mathematical and information-theoretic techniques such as RLE, Huffman Coding, DCT, Quantization and JPEG.

1️⃣ Upload Your Image

📁 Choose Image

No image selected.

2️⃣ Image for Compression Study

🖼️ Original Image

Upload an image to begin.

⚫ Grayscale Version

3️⃣ What is Image Compression?

Image compression reduces the number of bits required to represent an image while attempting to preserve the important visual information.

🟢 Lossless Compression

The reconstructed image is exactly identical to the original image. No information is permanently lost.

Examples: RLE, Huffman, LZW, PNG.

🟠 Lossy Compression

Some information is discarded to obtain a much smaller file. The reconstructed image is usually an approximation.

Example: JPEG.

🔵 Redundancy

Compression removes different forms of redundancy:

• Spatial redundancy
• Statistical redundancy
• Psychovisual redundancy

🟣 Main Goal

Reduce storage and transmission requirements while maintaining acceptable image quality.

4️⃣ Major Compression Techniques

Technique	Type	Main Idea	Typical Use
RLE	Lossless	Encode repeated values	Simple images
Huffman	Lossless	Short codes for frequent symbols	JPEG / general coding
LZW	Lossless	Dictionary-based coding	GIF / TIFF
DPCM	Predictive	Encode prediction error	Image signals
DCT	Transform	Convert spatial data to frequency coefficients	JPEG
DWT	Transform	Wavelet decomposition	JPEG 2000
Quantization	Lossy	Reduce coefficient precision	JPEG

5️⃣ Compression Ratio

Compression ratio compares the original size with the compressed size.

CR = Original Size / Compressed Size

Percentage reduction:

Reduction = [(Original − Compressed) / Original] × 100%

Original Size

Compressed Size

Click Calculate Ratio.

6️⃣ Run-Length Encoding (RLE)

RLE is a simple lossless compression technique. Instead of storing every repeated pixel separately, it stores the value and the number of consecutive repetitions.

AAAAABBBCCAAAA

becomes:

A5 B3 C2 A4

🔬 Interactive RLE Demonstration

Enter a sequence and click Compress.

7️⃣ Entropy and Image Compression

Entropy measures the average information contained in the image intensity distribution.

H = −Σ pᵢ log₂(pᵢ)

For an 8-bit grayscale image:

0 ≤ H ≤ 8 bits/pixel

Important: Lower statistical redundancy generally provides less opportunity for lossless compression, while predictable/repetitive data is often more compressible. Entropy provides a theoretical lower bound for average lossless coding length under the appropriate source model.

8️⃣ DCT — Discrete Cosine Transform

JPEG divides an image into 8×8 blocks and applies the Discrete Cosine Transform to represent spatial intensity variations as frequency coefficients.

F(u,v) = (1/4) C(u)C(v) ΣΣ f(x,y) cos[(2x+1)uπ/16] cos[(2y+1)vπ/16]

where:

f(x,y)

Original 8×8 image block.

F(u,v)

DCT frequency coefficient.

u,v

Frequency coordinates.

C(u), C(v)

Normalization factors.

8×8 Image Block

The upper-left coefficient is called the DC coefficient. Other coefficients represent increasing spatial frequencies.

9️⃣ Quantization

Quantization reduces the precision of DCT coefficients. This is the major stage responsible for information loss in typical JPEG compression.

Q(u,v) = round[F(u,v) / Q_table(u,v)]

Larger quantization values produce stronger compression but usually lower image quality.

Small Quantization

More coefficients are retained.

Higher quality → Larger file

Large Quantization

More coefficients become zero or smaller.

Lower quality → Smaller file

🔟 JPEG Compression Pipeline

🖼️
Image

➜

🌈
RGB → YCbCr

➜

⬛
8×8 Blocks

➜

📐
DCT

➜

➗
Quantization

➜

↗️
Zig-Zag

➜

🔢
RLE

➜

🌳
Huffman

➜

📦
Compressed

Click Start JPEG Animation.

1️⃣1️⃣ Measuring Compression Quality

Mean Squared Error — MSE

MSE = (1/MN) ΣΣ [I(i,j) − K(i,j)]²

where I is the original image and K is the reconstructed image.

Peak Signal-to-Noise Ratio — PSNR

PSNR = 10 log₁₀(MAX²/MSE)

For an 8-bit image:

MAX = 255

Generally: Lower MSE means less pixel-wise error. Higher PSNR generally indicates better reconstruction quality.

1️⃣2️⃣ Lossless vs Lossy Compression

Feature	Lossless	Lossy
Information loss	No	Yes
Reconstruction	Exactly original	Approximation
Compression ratio	Usually lower	Usually higher
Quality degradation	None	Possible
Examples	PNG, RLE, Huffman, LZW	JPEG
Best suited for	Medical/technical graphics, text-like images	Photographs and natural scenes

1️⃣3️⃣ Three Types of Redundancy

1. Statistical Redundancy

Some symbols occur much more frequently than others. Huffman coding can exploit this redundancy.

2. Spatial Redundancy

Neighboring pixels often have similar values. Predictive and transform techniques can exploit this property.

3. Psychovisual Redundancy

Human vision does not perceive all image information equally. Lossy techniques can discard some less perceptually important information.

1️⃣4️⃣ Applications

📱 Mobile Applications

Reducing image size saves storage and network bandwidth.

🌐 Web Images

Compressed images improve page loading and reduce bandwidth usage.

🛰️ Satellite Images

Compression reduces the amount of data that must be transmitted.

🏥 Medical Imaging

Lossless methods are particularly important when exact pixel information must be preserved.

📹 Video Processing

Image compression principles form an important foundation for video compression systems.

☁️ Cloud Storage

Compression can reduce storage requirements for large image collections.

🎯 Complete Compression Flow

Image → Remove Redundancy → Transform / Predict → Quantize → Entropy Coding → Compressed Image

Important Formulas

Compression Ratio = Original Size / Compressed Size

H = −Σpᵢlog₂(pᵢ)

MSE = (1/MN)ΣΣ[I(i,j)−K(i,j)]²

PSNR = 10log₁₀(255²/MSE)

Created by Bijan Krishna Paul
B.K.PAUL Bhairab Ganguly College