Compression

Image compression reduces the storage space and bandwidth required to save and transmit images. Without compression, a 24-megapixel RGB image would occupy approximately 72 megabytes. Compression algorithms identify patterns, redundancies, and imperceptible details that can be encoded more efficiently or discarded entirely. The tradeoff between file size and image quality is the central consideration in choosing compression settings for any application.\n\nWeb developers balance compression against page load speed. An e-commerce site with hundreds of product images must keep total page weight manageable for fast loading, especially on mobile connections. Each image is compressed to the smallest file size that maintains acceptable visual quality. Modern formats like WebP and AVIF achieve better quality-to-size ratios than older JPEG, allowing sites to serve higher-quality images at the same bandwidth cost.\n\nCompression artifacts become visible when images are compressed too aggressively. JPEG compression at very low quality settings produces characteristic blocky artifacts (8×8 pixel grid patterns), color banding in gradients, and mosquito noise around high-contrast edges. These artifacts are particularly visible in areas with subtle color transitions like blue skies and skin tones. Once compression artifacts are baked into an image, they cannot be fully removed, though AI enhancement can reduce their visibility.\n\nMagic Eraser outputs images in standard formats (PNG, WebP, JPEG) with appropriate compression for each use case. Transparent background exports use lossless PNG or WebP to preserve edge quality. Standard edits use balanced compression that maintains visual quality at reasonable file sizes. The AI enhancement pipeline also mitigates compression artifacts present in uploaded source images. When users upload heavily compressed images from messaging apps or social media downloads, the AI identifies and reduces visible JPEG blocking, color banding, and edge ringing artifacts, partially restoring quality that was lost during previous compression cycles and producing cleaner output than the degraded input source.