Which statement correctly defines data compression, the difference between lossless and lossy compression, and provides a common algorithm example for each?

Which statement correctly defines data compression, the difference between lossless and lossy compression, and provides a common algorithm example for each?

• A. Compression increases data size.
• B. Lossy compression preserves all data exactly.
• C. Lossless compression preserves all data; lossy compression discards some data; examples: Huffman coding for lossless; JPEG for lossy.
• D. JPEG is lossless.

Answer: (C)

Data compression means encoding information using fewer bits or bytes than the original, so storage or transmission requires less space. The important distinction is whether you can recreate the exact original data or not. Lossless compression preserves every bit of the original data, so after decompressing you get exactly what you started with. Lossy compression drops some information to achieve higher reduction in size, so the decompressed data is an approximation rather than a perfect copy, though it can be visually or functionally very close for many uses. A common lossless technique is Huffman coding, which assigns shorter codes to more frequent symbols and longer codes to rarer ones, allowing the original sequence to be perfectly reconstructed during decompression. For lossy compression, JPEG is a well-known example in images. It uses transformations and quantization to discard fine detail and color information that are less noticeable to the human eye, producing a much smaller file at the cost of some loss of fidelity. So the statement that correctly defines data compression, distinguishes lossy from lossless, and gives typical algorithm examples is the one that says lossless preserves all data and lossy discards some data, with Huffman coding as an example of lossless and JPEG as an example of lossy. The other options either claim compression increases size, misstate what lossless or lossy means, or claim JPEG is lossless.

Data compression means encoding information using fewer bits or bytes than the original, so storage or transmission requires less space. The important distinction is whether you can recreate the exact original data or not. Lossless compression preserves every bit of the original data, so after decompressing you get exactly what you started with. Lossy compression drops some information to achieve higher reduction in size, so the decompressed data is an approximation rather than a perfect copy, though it can be visually or functionally very close for many uses.

A common lossless technique is Huffman coding, which assigns shorter codes to more frequent symbols and longer codes to rarer ones, allowing the original sequence to be perfectly reconstructed during decompression. For lossy compression, JPEG is a well-known example in images. It uses transformations and quantization to discard fine detail and color information that are less noticeable to the human eye, producing a much smaller file at the cost of some loss of fidelity.

So the statement that correctly defines data compression, distinguishes lossy from lossless, and gives typical algorithm examples is the one that says lossless preserves all data and lossy discards some data, with Huffman coding as an example of lossless and JPEG as an example of lossy. The other options either claim compression increases size, misstate what lossless or lossy means, or claim JPEG is lossless.