Bezpieczeństwo i zaufanie

Key Features & Capabilities

Step-by-Step How To Guide

1. Step 1: Upload or Drag & Drop - Select your image files from your computer, tablet, or smartphone. Multiple files are supported for batch processing.
2. Step 2: Configure Settings - Choose your desired tool, adjustment sliders, background colors, compression thresholds, or watermark parameters.
3. Step 3: Instant Processing & Download - Preview results in real-time with our interactive before/after slider and download your crystal-clear image in full resolution.

Architecture & Technical Specifications

HelpMyIMG utilizes modern browser APIs including WebAssembly (WASM), WebGPU, OffscreenCanvas, and dedicated Web Workers. Machine learning models such as Bria RMBG-1.4 and MediaPipe vision tasks execute in an isolated background thread, ensuring your main UI thread remains smooth and 100% responsive at 60 FPS.

• Client-Side Neural Network Inference: ONNX Runtime WASM via @huggingface/transformers.
• Lossless & Lossy Compression Engine: Browser-native Canvas 2D with smart chroma subsampling.
• Data Privacy Guarantee: Strict GDPR and CCPA compliance by never sending image payload bytes across the internet.

Zasady algorytmiczne i podstawy recenzowane

HelpMyIMG powstał w oparciu o otwarte, recenzowane badania nad wizją komputerową i systemami rozproszonymi. Nasz proces dostępny w przeglądarce implementuje modele matematyczne opublikowane przez wiodące instytucje akademickie.

• Neural Matting & Saliency: Ke et al., "MODNet: Real-Time Trimap-Free Portrait Matting via Objective Decomposition", AAAI 2022 / Qin et al., "Highly Accurate Dichotomous Image Segmentation (IS-Net / DIS)", ECCV 2022.
• Perceptual Quality & SSIM: Wang et al., "Image Quality Assessment: From Error Visibility to Structural Similarity (SSIM)", IEEE Transactions on Image Processing (IEEE TIP), 2004.
• In-Browser WebAssembly Acceleration: Haas et al., "Bringing the Web up to Speed with WebAssembly", ACM SIGPLAN PLDI 2017.
• Local-First Privacy Architecture: Kleppmann et al., "Local-First Software: You Own Your Data, in Spite of the Cloud", ACM SIGPLAN Onward! 2019.
• Ultra-Fast Neural Face Detection: Bazarevsky et al., "BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs", Google Research, CVPR Workshop 2019.

Informacja o przypisaniu akademickim: Cytaty i odniesienia do instytucji podano wyłącznie w celu zapewnienia przejrzystości naukowej i przypisania otwartych podstaw algorytmicznych. Nie oznaczają one bezpośredniego poparcia lub recenzji HelpMyIMG przez wskazanych autorów lub instytucje.

Frequently Asked Questions