Practice — Case Study: Super-Resolution and Photo Restoration (6 questions)

An interviewer says: "Design the feature that upscales and restores users' photos." A candidate immediately starts sketching a single RRDB-based GAN generator with an ESRGAN-style loss and asks what resolution to target.

1. What is missing from the candidate's framing before any architecture discussion should start?
2. Propose concrete assumption-table numbers (latency, upscale factor, compute location) for the two product points this prompt actually requires, and explain why a single set of numbers can't cover both.
3. A teammate suggests including NVIDIA DLSS-style real-time game upscaling as a third product point to design in full. Should you? Explain what you'd say instead.

Your team ships a new checkpoint of the on-device upscaling model. Offline, it improves PSNR by 0.8 dB and SSIM by 0.02 over the previous checkpoint — a clear win by the metrics the team has always tracked. After rollout, user complaints about "blurry" or "fake-looking" zoomed photos rise, and the regeneration/re-crop rate in the cloud restoration product (a related but separate model family) also ticks up over the same period.

1. Explain, using the perception-distortion trade-off, how a model can improve PSNR/SSIM while making users less happy with the result.
2. What evaluation should have caught this before rollout, and why didn't PSNR/SSIM catch it?
3. Propose a decision rule for what "improved" should mean for this product going forward, and justify it.