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This deck focuses on Data Compression, giving you a quick way to review the definitions, rules, and examples that matter most for AP Computer Science Principles.
Study Data Compression in AP Computer Science Principles with focused flashcards that help you recognize the idea, recall the key rule, and apply it in practice-style prompts.
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Which compression type typically results in smaller file sizes?
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Lossy compression. Removing data allows for greater size reduction.
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This deck focuses on Data Compression, giving you a quick way to review the definitions, rules, and examples that matter most for AP Computer Science Principles.
Work through these flashcards in short sessions. Try to answer each prompt before flipping the card, then revisit any cards you miss until the explanation feels automatic.
Answer: Lossy compression. Removing data allows for greater size reduction.
Answer: Lossless compression. File archives require perfect data preservation.
Answer: Larger file sizes compared to lossy compression. Perfect preservation requires more storage space.
Answer: Data with high entropy. Random data lacks patterns for compression algorithms.
Answer: File size versus quality. Core decision in choosing compression settings.
Answer: Data compression reduces file size by encoding information more efficiently. Essential technique for managing storage and bandwidth efficiently.
Answer: Balancing compression efficiency with data quality. Optimizing both compression ratio and acceptable quality.
Answer: The JPEG algorithm. Widely used for image compression with quality loss.
Answer: Restoring compressed data to its original form. Process of expanding compressed data back to original.
Answer: Streaming audio or video where quality loss is acceptable. Real-time applications can tolerate some quality loss.
Answer: When data integrity is critical, such as text files. Perfect reconstruction is essential for text documents.
Answer: When data integrity is critical, such as text files. Perfect reconstruction is essential for text documents.
Answer: Compression that allows original data to be perfectly reconstructed. No information is permanently lost during compression.
Answer: Measure of randomness or disorder in data. Higher entropy means less predictable, harder to compress data.
Answer: JPEG. Common image format that sacrifices quality for size.
Answer: Faster data transmission. Reduced file sizes improve network transfer speeds.
Answer: A lossless data compression algorithm using variable-length codes. Assigns shorter codes to more frequent characters.
Answer: Lossless compression. Only lossless compression preserves all original data.
Answer: Lossless compression. File archives require perfect data preservation.
Answer: Lossy compression. Audio quality reduction is acceptable for smaller files.
Answer: Measure of randomness or disorder in data. Higher entropy means less predictable, harder to compress data.
Answer: To reduce the size of data files. Fundamental objective of all compression algorithms.
Answer: Streaming audio or video where quality loss is acceptable. Real-time applications can tolerate some quality loss.
Answer: A lossless data compression algorithm using variable-length codes. Assigns shorter codes to more frequent characters.
Answer: Ratio of the size of the compressed file to the original file. Lower ratios indicate better compression efficiency.
Answer: Potential loss of detail in lossy compression. Lossy algorithms remove visual details to reduce size.
Answer: Lossy compression. Removing data allows for greater size reduction.
Answer: Moving Picture Experts Group. Standards organization for audio and video compression.
Answer: Ratio of the size of the compressed file to the original file. Lower ratios indicate better compression efficiency.
Answer: A simple form of lossless data compression based on runs of data. Replaces repeated values with count and value pairs.
Answer: The Lempel-Ziv-Welch (LZW) algorithm. Dictionary-based algorithm for efficient lossless compression.
Answer: Lossy compression. Audio quality reduction is acceptable for smaller files.
Answer: Compression that reduces file size by removing some data, losing quality. Trades data fidelity for smaller file sizes.
Answer: Moving Picture Experts Group. Standards organization for audio and video compression.
Answer: Higher redundancy increases potential for compression. Repeated patterns enable more effective compression.
Answer: Data with low entropy. Predictable data contains patterns for compression.
Answer: Balancing compression efficiency with data quality. Optimizing both compression ratio and acceptable quality.
Answer: Medical imaging where data integrity is crucial. Critical applications require perfect data preservation.
Answer: Lossy and lossless compression. These represent the fundamental approaches to data reduction.
Answer: Data with high entropy. Random data lacks patterns for compression algorithms.
Answer: Loss of data quality and fidelity. Irreversible information loss reduces file quality.
Answer: The Lempel-Ziv-Welch (LZW) algorithm. Dictionary-based algorithm for efficient lossless compression.
Answer: The JPEG algorithm. Widely used for image compression with quality loss.
Answer: PNG. Image format that preserves all original pixel data.
Answer: Number of bits processed per second in a compressed file. Determines data transmission speed and quality.
Answer: Larger file sizes compared to lossy compression. Perfect preservation requires more storage space.
Answer: Number of bits processed per second in a compressed file. Determines data transmission speed and quality.
Answer: Compression generally increases data transmission speed. Smaller files transfer faster over networks.
Answer: The importance of data fidelity. Critical consideration when selecting compression type.
Answer: It decreases the file size. Lower bit rates mean more compression and smaller files.
Answer: To reduce the size of data files. Fundamental objective of all compression algorithms.
Answer: A device or program for encoding or decoding data streams. Software that compresses or decompresses digital media.
Answer: The importance of data fidelity. Critical consideration when selecting compression type.
Answer: Higher entropy indicates less potential for compression. Random data has high entropy and resists compression.
Answer: Medical imaging where data integrity is crucial. Critical applications require perfect data preservation.
Answer: Compression that reduces file size by removing some data, losing quality. Trades data fidelity for smaller file sizes.
Answer: Compression that allows original data to be perfectly reconstructed. No information is permanently lost during compression.
Answer: 4:1. Original size divided by compressed size: 200÷50=4.
Answer: File size versus quality. Core decision in choosing compression settings.
Answer: Faster data transmission. Reduced file sizes improve network transfer speeds.
Answer: Data compression reduces file size by encoding information more efficiently. Essential technique for managing storage and bandwidth efficiently.
Answer: The file may not be identical to the original. Lossy compression causes permanent information loss.
Answer: Loss of data quality and fidelity. Irreversible information loss reduces file quality.
Answer: The file is identical to the original. Lossless compression preserves every bit of data.