--- title: "Lossy or lossless" subtitle: "Making data smaller · Compression · Lesson 2" --- # Lossy or lossless **Name:** {{field-24}} **Date:** {{field-15}} **Class:** {{field-15}} > **Key question:** A song streamed on your phone is about a tenth of the size of the same song on a CD. What did they throw away, and why can't you hear it? :::teacher-note **In one sentence:** Students raise the tolerance of a lossy run-length compressor on a grey picture to see that lossy compression stores far fewer numbers but permanently changes data, and decide where lossy and lossless each belong (AC9TDI10K03 node 3). **Students should be able to say:** I can explain the difference between lossy and lossless compression. I can choose lossy or lossless for a purpose and justify it. I can name a consequence of using lossy compression in the wrong place. **Width:** the unmarked parts are the floor every student does (predict, run, investigate and make). Typical adds the modify practice; wider adds the real-world contexts. They add contexts, not difficulty. Students work in the online tutorial alongside this sheet. ::: ## Predict :::keep-together **A grey photo is compressed by treating nearly equal shades as the same, so the runs get longer. Then it is opened again. Is it exactly the same as the original?** ☐ No: it comes back black and white ☐ Yes, if you open it twice ☐ No: the small differences are gone for good ☐ Yes: compression always gives back the original My prediction, and why: {{lines-3}} ::: :::teacher-note **What happens:** No. Shades that were treated as the same are stored as one shade, and the original values were never saved, so nothing can put them back. ::: ## Run Try it on the board in the tutorial. Was your prediction right? What was different? {{lines-2}} ## Worked example: lossless versus lossy :::keep-together ### The trade-off 1. $t = 0: 28 \text{ numbers}, 0 \text{ changed}$ — Lossless: only exact runs. Already smaller than 48. 2. $t = 28: 18 \text{ numbers}, 13 \text{ changed}$ — Shades next to each other (28 apart) now count as the same. 3. $t = 40: 16 \text{ numbers}, 16 \text{ changed}$ — Smaller again, and a third of the pixels are a different shade. 4. $\text{lossy}$ — The changed pixels were never stored, so they can't be recovered. 5. $\text{choose by purpose}$ — Lossy where small changes don't matter; lossless where every value counts. Now define lossless. **What makes compression **lossless**?** ☐ Decompressing gives back exactly the original data ☐ The file gets smaller ☐ It uses run-length encoding ☐ Nobody can notice the difference ::: :::teacher-note **Answer:** Decompressing gives back exactly the original data Lossless means exact recovery. Lossy means some data is permanently discarded. ::: :::keep-together ### Find the mistake **Sam explains what happens to a lossy photo. Click the line where it first goes wrong.** ``` 1 I save a photo in a lossy format. 2 The file is a quarter of the size. 3 When I open it, the computer puts the lost detail back. 4 So the photo is exactly the same as before. ``` Line: {{field-15}} Why is it wrong? {{lines-2}} ::: :::teacher-note **Answer:** line 3 Line 3: the lost detail was never stored, so nothing can put it back. A lossy photo looks close to the original but is not the same. ::: :::keep-together ### Which must be lossless? **Which of these must be stored **lossless**?** ☐ A medical X-ray a doctor will use to find a fracture ☐ A holiday photo shared online ☐ A video call ☐ A song streamed on a phone ::: :::teacher-note **Answer:** A medical X-ray a doctor will use to find a fracture A change in a few pixels could hide or invent a detail a doctor relies on. Medical images, legal evidence, text and spreadsheets need every value exact. ::: :::typical ## Vary: one setting at a time **Back to lossless.** **All the way.** **Worth it?.** **Streaming.** Work in the tutorial. Plan first: what will you change, and how will you test it? {{box-4}} ::: ## Choose a format :::keep-together ### Formats **Which format is **lossless**?** ☐ PNG ☐ A video streamed at low quality ☐ JPEG ☐ MP3 ::: :::teacher-note **Answer:** PNG PNG (and ZIP) keep every value; JPEG and MP3 are lossy. ::: :::keep-together ### Evidence **A police photo of a number plate is saved as a heavily compressed lossy file. What could go wrong?** ☐ The file could be too big ☐ Nothing: lossy files look the same ☐ Letters could blur into each other, so the evidence is unclear or misread ☐ The photo would turn black and white ::: :::teacher-note **Answer:** Letters could blur into each other, so the evidence is unclear or misread Evidence needs every detail; lossy compression can change exactly the details that matter. ::: :::keep-together ### Save again **You open a JPEG, edit it and save it as a JPEG again, many times. What happens?** ☐ It loses a little more detail each time ☐ It turns into a PNG ☐ It gets sharper ☐ It stays exactly the same ::: :::teacher-note **Answer:** It loses a little more detail each time Lossy losses add up. Keep a lossless original and make lossy copies from it. ::: :::keep-together ### Review: run-length codes **Review: what is the run-length code for white, white, white, white, black, black, black, black?** ☐ 8 ☐ 4, 4 ☐ 1, 1, 1, 1, 1, 1, 1, 1 ☐ 0, 4, 4 ::: :::teacher-note **Answer:** 4, 4 4 white, 4 black. ::: ## Make: the edge of lossless Then: would that tolerance work for a different picture? Why not? Work in the tutorial. Plan first: what will you change, and how will you test it? {{box-4}} ## Exit ticket :::keep-together **Give one context that needs lossless compression and one where lossy is fine, with a reason for each.** {{lines-3}} ::: :::teacher-note **Answer:** Lossless: a medical scan or spreadsheet, where every value matters. Lossy: streamed music or a shared photo, where a much smaller file matters more than tiny changes. :::