Compression

    AQA
    GCSE

    Compression algorithms reduce file sizes to optimize storage capacity and minimize transmission latency across networks. Candidates must distinguish between lossy compression, which permanently discards non-essential data (e.g., perceptual redundancy in images/audio), and lossless compression, which retains complete data integrity using algorithms like Run Length Encoding (RLE) and Huffman coding. Mastery requires calculating compression ratios and justifying the selection of specific algorithms based on data type (text vs. multimedia) and the imperative for data accuracy versus file size reduction.

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    Objectives
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    Exam Tips
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    Pitfalls
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    Key Terms
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    Mark Points

    What You Need to Demonstrate

    Key skills and knowledge for this topic

    • Award 1 mark for stating that compression reduces file size to decrease transmission time over a network or save storage space
    • Credit responses that identify Lossless compression as essential for text or code files where data loss renders the file unusable
    • Award marks for correctly deriving a bit pattern from a provided Huffman tree, typically following the convention of 0 for left and 1 for right branches
    • For Run Length Encoding (RLE), award 1 mark for correctly identifying the frequency-value pairs (e.g., converting 'AAAB' to '3A 1B')
    • Award 1 mark for explaining that Lossy compression permanently removes non-essential data to achieve significantly smaller file sizes

    Marking Points

    Key points examiners look for in your answers

    • Award 1 mark for stating that compression reduces file size to decrease transmission time over a network or save storage space
    • Credit responses that identify Lossless compression as essential for text or code files where data loss renders the file unusable
    • Award marks for correctly deriving a bit pattern from a provided Huffman tree, typically following the convention of 0 for left and 1 for right branches
    • For Run Length Encoding (RLE), award 1 mark for correctly identifying the frequency-value pairs (e.g., converting 'AAAB' to '3A 1B')
    • Award 1 mark for explaining that Lossy compression permanently removes non-essential data to achieve significantly smaller file sizes

    Examiner Tips

    Expert advice for maximising your marks

    • 💡When calculating Huffman savings, create a table: Character | Frequency | Bit Length | Total Bits. This prevents arithmetic errors and secures method marks
    • 💡Always justify the choice of compression type by referencing the specific content (e.g., 'The image can lose detail without the user noticing' vs 'The code must be exact')
    • 💡Check the direction of the Huffman tree branches carefully; do not assume left is always 0 unless the key or question explicitly specifies it

    Common Mistakes

    Pitfalls to avoid in your exam answers

    • Incorrectly suggesting Lossy compression is suitable for text documents or Python scripts, failing to recognize that removing data makes them unreadable
    • In Huffman coding, counting the number of nodes in the tree rather than multiplying the frequency of each character by its specific bit length to find total size
    • Assuming RLE always results in a smaller file size, ignoring scenarios where non-repeating data causes the 'compressed' file to be larger than the original due to metadata overhead

    Key Terminology

    Essential terms to know

    Likely Command Words

    How questions on this topic are typically asked

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