Data compression is the process of encoding information using fewer bits than the original representation, making it more efficient for storage and transmission. It works by identifying and eliminating redundancy in data, such as repeated patterns or unnecessary details.
There are two main types: lossless compression, which allows the original data to be perfectly reconstructed (e.g., ZIP files, PNG images), and lossy compression, which discards some data to achieve higher reduction rates, acceptable for applications where minor losses are imperceptible (e.g., JPEG images, MP3 audio).
Common techniques include run-length encoding, which replaces sequences of identical data with a single value and a count; Huffman coding, which assigns shorter codes to more frequent symbols; and algorithms like LZW for text or DCT for images in lossy scenarios.
Data compression is essential in various fields: it reduces file sizes for faster internet downloads, optimizes storage on devices, enables efficient video streaming, and supports big data processing. For instance, it’s used in web browsers, databases, and telecommunications to save bandwidth and resources.
Benefits include improved efficiency, cost savings, and faster data handling, but drawbacks may involve increased processing time during compression/decompression and potential quality loss in lossy methods. As data volumes grow, advanced algorithms like those in modern formats (e.g., HEVC for video) continue to evolve for better performance.
Table of Contents
- Part 1: Create A Data Compression Quiz in Minutes Using AI with OnlineExamMaker
- Part 2: 20 Data Compression Quiz Questions & Answers
- Part 3: OnlineExamMaker AI Question Generator: Generate Questions for Any Topic
Part 1: Create A Data Compression Quiz in Minutes Using AI with OnlineExamMaker
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Part 2: 20 Data Compression Quiz Questions & Answers
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Question 1:
What type of compression allows the original data to be perfectly reconstructed?
A) Lossy compression
B) Lossless compression
C) Run-length encoding
D) Huffman coding
Answer: B
Explanation: Lossless compression techniques ensure that the decompressed data is identical to the original, making it suitable for applications like text files where no data loss is acceptable.
Question 2:
In Huffman coding, how are shorter codes assigned?
A) To the most frequent symbols
B) To the least frequent symbols
C) Randomly
D) Based on symbol length
Answer: A
Explanation: Huffman coding uses a variable-length prefix code where shorter codes are given to more frequent symbols to minimize the overall data size.
Question 3:
Which compression method is commonly used for text files and involves replacing repeated characters with a single character and a count?
A) LZW compression
B) JPEG compression
C) Run-length encoding
D) Arithmetic coding
Answer: C
Explanation: Run-length encoding compresses data by storing sequences of the same value as a single value and a count, making it efficient for data with many repeats, like in simple images or text.
Question 4:
What is the main advantage of lossy compression over lossless compression?
A) It preserves all original data
B) It achieves higher compression ratios
C) It is faster to decompress
D) It works only on text files
Answer: B
Explanation: Lossy compression discards some data that is less perceptible to humans, allowing for significantly smaller file sizes, which is ideal for media like images and audio.
Question 5:
In LZW compression, what does the algorithm build during the encoding process?
A) A frequency table
B) A dictionary of strings
C) A binary tree
D) A run-length count
Answer: B
Explanation: LZW compression dynamically builds a dictionary of substrings as it processes the data, replacing repeated sequences with shorter codes for efficiency.
Question 6:
Which of the following is an example of a lossless compression algorithm?
A) MP3
B) JPEG
C) ZIP
D) MPEG
Answer: C
Explanation: ZIP uses algorithms like DEFLATE, which are lossless, ensuring the original file can be restored exactly, unlike lossy formats like MP3 or JPEG.
Question 7:
What does entropy represent in the context of data compression?
A) The average length of compressed data
B) The minimum number of bits needed to represent data
C) The file size before compression
D) The compression ratio
Answer: B
Explanation: Entropy measures the uncertainty or information content in data, indicating the theoretical lower bound of bits required for lossless compression.
Question 8:
Which technique is used in ZIP files and combines LZ77 with Huffman coding?
A) Arithmetic coding
B) DEFLATE
C) LZW
D) Run-length encoding
Answer: B
Explanation: DEFLATE algorithm in ZIP files uses LZ77 for string matching and Huffman coding for encoding, providing effective compression for various file types.
Question 9:
In lossy compression, what is typically sacrificed to achieve smaller file sizes?
A) Data accuracy
B) File metadata
C) Compression speed
D) Header information
Answer: A
Explanation: Lossy compression intentionally removes less important data, such as high-frequency sounds in audio, to reduce size while maintaining perceptual quality.
Question 10:
What is the purpose of a prefix code in compression algorithms like Huffman?
A) To allow variable-length codes without ambiguity
B) To fix the code length for all symbols
C) To encrypt the data
D) To add error correction
Answer: A
Explanation: Prefix codes ensure that no code is a prefix of another, allowing unambiguous decoding of variable-length codes in algorithms like Huffman coding.
Question 11:
Which compression method is often used for GIF images?
A) JPEG
B) LZW
C) Arithmetic coding
D) Run-length encoding
Answer: B
Explanation: LZW compression is used in GIF files to achieve lossless compression by building a dictionary of patterns found in the image data.
Question 12:
How does arithmetic coding differ from Huffman coding?
A) It assigns codes based on fixed lengths
B) It treats the entire message as a single number
C) It only works on binary data
D) It requires a dictionary
Answer: B
Explanation: Arithmetic coding encodes the entire input as a single fractional number between 0 and 1, potentially achieving better compression than Huffman for certain data distributions.
Question 13:
What factor most influences the effectiveness of compression algorithms?
A) The type of hardware used
B) The data’s redundancy and patterns
C) The file’s name length
D) The operating system
Answer: B
Explanation: Compression works best on data with high redundancy, such as repeated patterns or predictable sequences, allowing algorithms to reduce size efficiently.
Question 14:
In video compression like MPEG, what technique is used to reduce temporal redundancy?
A) Color quantization
B) Inter-frame prediction
C) Huffman encoding
D) Run-length encoding
Answer: B
Explanation: Inter-frame prediction in MPEG compares frames and only stores differences, reducing redundancy between consecutive frames in a video sequence.
Question 15:
Which of the following is a disadvantage of lossless compression?
A) It may not achieve high compression ratios
B) It alters the original data
C) It is slower than lossy methods
D) It only works on images
Answer: A
Explanation: Lossless compression preserves all data, which limits its ability to reduce file sizes as much as lossy methods, especially for complex data like photos.
Question 16:
What is the role of a sliding window in LZ77 compression?
A) To store the entire dictionary
B) To look for repeated strings in previous data
C) To encode single characters
D) To calculate entropy
Answer: B
Explanation: LZ77 uses a sliding window to search for and replace repeated strings from the recently processed data, improving compression by referencing matches.
Question 17:
Which algorithm is probabilistic and can achieve compression close to the entropy limit?
A) Run-length encoding
B) Arithmetic coding
C) Huffman coding
D) LZW
Answer: B
Explanation: Arithmetic coding is a probabilistic method that can compress data very close to its entropy by representing symbols with fractional bits through interval mapping.
Question 18:
For which type of data is run-length encoding most effective?
A) Random data with no patterns
B) Data with long sequences of identical elements
C) Audio files
D) Encrypted files
Answer: B
Explanation: Run-length encoding excels with data that has many consecutive repeats, such as in simple graphics or fax transmissions, by counting the runs.
Question 19:
In JPEG compression, what process is used to reduce spatial redundancy in images?
A) Discrete Cosine Transform (DCT)
B) Huffman coding only
C) LZW dictionary building
D) Inter-frame prediction
Answer: A
Explanation: JPEG uses DCT to transform image blocks into frequency components, allowing it to discard high-frequency details for lossy compression.
Question 20:
What is the primary goal of using compression in data transmission?
A) To increase transmission time
B) To reduce bandwidth requirements
C) To add security layers
D) To complicate data decoding
Answer: B
Explanation: Compression minimizes the amount of data sent over networks, thereby reducing bandwidth usage and improving transmission efficiency.
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