Image Processing

7 posts

toss3 min readCurated summary

If You Asked a Designer to Make Anything with AI

Toss Design Chapter’s AI Contest invited designers to build anything with AI, resulting in 122 projects over one month. The examples show that designers primarily used AI to improve existing work—making it faster, more persuasive, and higher quality—rather than creating entirely new kinds of work. The article recommends starting with a frustrating, repetitive task or a frequently repeated communication problem. ## Automating Repetitive Work - A color-extraction tool automatically identifies and adjusts colors from images for use in UI. - Color extraction had been an unresolved challenge at Toss because results varied widely by image. - Designers used AI to draft the logic, test it against many sample images, and rapidly refine it. - The resulting system is now used for product-card colors in Toss Shopping. ## Reducing Collaboration Costs with a Personal Bot - A Slack bot was trained on a designer’s knowledge, past discussions, and reference materials. - It creates draft answers to the many design and requirements questions the designer receives each day. - Team members can send the draft as-is or revise it before responding. - The bot learns from those revisions, improving its answers to similar questions over time. - The designer described the result as feeling like becoming “1.5 people,” and other Toss designers began creating their own bots. ## Persuading Through Interactive Prototypes - A designer built a functioning prototype of a stock-trading desktop interface instead of presenting only static screens. - Users could drag panels, rearrange them, and resize windows, with the interface responding accordingly. - Showing the intended interactions directly reduced the risk that design ideas would be misunderstood during development. - The working prototype helped align designers and developers and persuade the product owner. ## Pushing Quality Within Tight Deadlines - AI-generated motion graphics were created for the key visual of Toss Bank’s recruitment website. - Each job category needed its own animation despite a very short schedule. - The designer created the foundational images manually and repeatedly refined Kling prompts to achieve the desired results. - Human-designed starting and ending frames combined with AI-generated motion allowed all category animations to be completed in a single day. ## Four Ways to Start Using AI - **Efficiency:** Hand off one especially annoying repetitive task to AI. - **Replication:** Build a bot to answer questions you repeatedly handle yourself. - **Persuasion:** Turn designs that require verbal explanation into working prototypes. - **Quality:** Use AI to reach a higher level of polish within a limited timeframe. The practical recommendation is to begin with an existing task rather than searching for an entirely new AI application. Choose one area where AI can save time, communicate intent more clearly, or help raise the final quality.

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netflix3 min readCurated summary

Scaling Camera File Processing at Netflix

Netflix built its Media Production Suite (MPS) to automate repetitive media workflows, improve consistency, and give filmmakers more time for creative work. Rather than develop an image-processing engine internally, Netflix partnered with FilmLight and integrated its FilmLight API (FLAPI) into Netflix’s cloud infrastructure. This combination provides reliable, camera-aware processing at global scale while supporting open standards, auditability, and rapid turnaround. ## Why Netflix Built MPS - Netflix productions use a wide range of cameras, formats, workflows, regions, and vendors. - File-based workflows created recurring problems: - Manual file wrangling reduced creative time. - Media handling varied between productions. - Human-driven processes were difficult to audit. - Teams repeatedly rebuilt similar workflows. - MPS aims to: - Standardize media management and movement from production through post-production. - Improve efficiency, consistency, and quality control. - Reduce errors and non-creative administrative work. ## Choosing FilmLight’s Processing Engine - Building a complete image-processing engine would require long-term collaboration with camera manufacturers and the broader industry. - Netflix needed a system that could: - Inspect, trim, and transcode camera-original files. - Preserve trusted color science and metadata. - Support many current and future camera formats. - Run within Netflix’s scalable, observable encoding infrastructure. - FilmLight’s Baselight and Daylight products already serve professional color grading, dailies, and transcoding workflows. - FLAPI allowed Netflix to use this proven processing technology as a backend API instead of duplicating it internally. ## Camera Metadata Inspection - Productions upload media with ASC Media Hash List (MHL) files to verify ingest completeness and integrity. - During the subsequent inspection phase, FLAPI: - Extracts metadata from original camera files. - Maps critical fields into Netflix’s normalized schema. - Makes the metadata searchable and reusable. - The metadata supports: - Matching footage by timing and reel name. - Automated retrieval. - Pipeline validation and troubleshooting. - Investigating why footage appears a certain way after processing. - Packaging FLAPI in Docker allows nearly identical deployments across Netflix’s cloud and global production compute environments. ## VFX Plates and Media Deliverables - MPS generates VFX plates and other outputs while preserving framing, color management, and camera-specific decoding behavior. - FLAPI is used to: - Debayer original camera files with format-appropriate parameters. - Crop and de-squeeze images according to ASC Framing Decision Lists. - Apply ACES Metadata Files for repeatable color workflows. - Produce deliverables in multiple formats. - The workflows are automated, repeatable, and auditable. - AMF files accompany OpenEXR outputs so recipients can identify which color transformations have already been applied. - Because the backend uses FilmLight technology, Netflix specialists can validate automated decisions in Baselight before production begins. ## Cloud-Native Media Processing - Traditional facilities often rely on powerful GPU systems and specialized high-performance storage. - Netflix instead designed its processing around the Cosmos compute and storage platform. - Cloud-compatible tools must: - Run as short-lived serverless functions in Linux Docker containers. - Operate effectively on CPU-only instances. - Support headless execution through Java, Python, or command-line interfaces. - Remain stateless so failed workers can be terminated and relaunched. - This model favors parallel processing across many workers rather than maximizing the power of one machine. - It improves cost and performance efficiency while maintaining production turnaround targets. - FLAPI’s API-driven, container-friendly, and low-state architecture made it straightforward for Netflix to integrate and operate reliably. Netflix’s approach demonstrates the value of combining established industry expertise with cloud-scale orchestration. By using FLAPI for specialized media processing and Cosmos for elastic execution, MPS can deliver consistent, traceable camera-file workflows without requiring Netflix to build and maintain every component itself.

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discordOriginal article

Discord Update: November 6, 2025 Changelog (opens in new tab)

The November 2025 Discord update focuses on streamlining content creation and enhancing the user interface across both desktop and mobile platforms. By removing technical barriers for emoji management and reorganizing core navigation, the platform aims to create a more intuitive experience for community interaction and personal expression. These changes signify a push toward visual consistency and greater flexibility in how users present themselves across different server environments. ### Streamlined Emoji Creation * A new integrated editing screen allows users to crop and resize large images directly during the upload process, eliminating the need for third-party photo editors. * The Emoji Picker now includes an "Add Emoji" shortcut for users with server permissions, allowing them to upload and assign icons to specific servers without leaving the chat interface. * Discord has automated the technical requirements for emoji uploads, removing the need for users to manually adjust files to specific resolutions (128x128), file types (PNG), or size limits before importing. ### Desktop Navigation and Utility * The desktop Settings menu is undergoing a visual refresh and reorganization to improve discoverability and match the platform's modern design language. * Voice channels on desktop now feature an active timer, providing a visible indicator of how long a specific call has been in progress. * The "More" section at the bottom of the Settings list serves as the new hub for accessing the Changelog and other platform documentation. ### Personalization and Mobile Features * Users can now set Nameplates on a per-server basis, allowing for professional appearances in some communities while maintaining more casual aesthetics in others. * The Discord Shop is now fully functional on mobile devices, enabling users to purchase and send gifts, Avatar Decorations, and bundles directly from tablets or phones. * Enhanced tools within the Family Center provide parents and guardians with updated oversight features to better monitor and engage with their teens' digital experiences. Server administrators and active users should take advantage of the new emoji upload tools to refresh their custom icons with less effort, while multi-community users can leverage the per-server Nameplates to better tailor their digital identity to different social contexts.

figma2 min readCurated summary

Introducing Three New Tools For Precise Image Editing In Figma | Figma Blog

Figma introduced three AI-powered image editing tools—Erase object, Isolate object, and Expand image—to make detailed image manipulation possible without leaving the design canvas. The tools complement existing features such as background removal, cropping, and AI image generation, helping designers refine assets in context. A new image-editing toolbar brings these capabilities together for faster, more integrated workflows. ## Erase and Isolate Objects - **Erase object** removes a selected object from an image. - **Isolate object** separates an object or person so it can be edited or repositioned without changing the background. - Users can select objects with a lasso and apply: - Lighting and color adjustments - Blur and focus effects - Color correction - Shadows - These tools are useful for refining product photos, removing distractions, and emphasizing key visual elements. - Text-prompt editing remains available through Figma’s **Edit image** feature, but the new tools provide more precise manual control. ## Expand Images for New Layouts - **Expand image** generates additional background content to fit a new aspect ratio. - It adapts images for formats such as: - Mobile layouts - Web banners - Social media assets - Unlike cropping, expansion preserves the original subject and surrounding context without distortion. - For example, a square product image can be expanded into a wide banner while leaving room for text. ## A Unified Image-Editing Toolbar - The new toolbar combines the three AI tools with existing capabilities, including: - Remove background - Crop - AI image generation and editing - Remove background is now easier to find because it is one of the most frequently used AI actions in Figma. - The tools are available across Figma, including FigJam, Slides, and Buzz beta, with some seat restrictions. - In Figma Design and Figma Draw, they are available to Full-seat users on Professional, Organization, and Enterprise plans with AI enabled. - AI actions consume Figma credits. These updates aim to keep image editing inside Figma, reducing the need to switch between external tools. Designers can now make precise object-level edits, adapt images to different formats, and maintain visual consistency directly within their workflows.

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naverOriginal article

Research on Protecting the Webtoon (opens in new tab)

Naver Webtoon is proactively developing technical solutions to safeguard its digital creation ecosystem against evolving threats like illegal distribution and unauthorized generative AI training. By integrating advanced AI-based watermarking and protective perturbation technologies, the platform successfully tracks content leaks and disrupts unauthorized model fine-tuning. These efforts ensure a sustainable environment where creators can maintain the integrity and economic value of their intellectual property. ## Challenges in the Digital Creation Ecosystem - **Illegal Content Leakage**: Unauthorized reproduction and distribution of digital content infringe on creator earnings and damage the platform's business model. - **Unauthorized Generative AI Training**: The rise of fine-tuning techniques (e.g., LoRA, Dreambooth) allows for the unauthorized mimicry of an artist's unique style, distorting the value of original works. - **Harmful UGC Uploads**: The presence of violent or suggestive user-generated content increases operational costs and degrades the service experience for readers. ## AI-Based Watermarking for Post-Tracking - To facilitate tracking in DRM-free environments, Naver Webtoon developed an AI-based watermarking system that embeds invisible signals into the pixels of digital images. - The system is designed around three conflicting requirements: **Invisibility** (signal remains hidden), **Robustness** (signal survives attacks like cropping or compression), and **Capacity** (sufficient data for tracking). - The technical pipeline involves three neural modules: an **Embedder** to insert the signal, a differentiable **Attack Layer** to simulate real-world distortions, and an **Extractor** to recover the signal. - Performance metrics show a high Peak Signal-to-Noise Ratio (PSNR) of over 46 dB, and the system maintains a signal error rate of less than 1% even when subjected to intense signal processing or geometric editing. ## IMPASTO: Disrupting Unauthorized AI Training - This technology utilizes **protective perturbation**, which adds microscopic changes to images that are invisible to humans but confuse generative AI models during the training phase. - It targets the way diffusion models (like Stable Diffusion) learn by either manipulating latent representations or disrupting the denoising process, preventing the AI from accurately mimicking an artist's style. - The research prioritizes overcoming the visual artifacts and slow processing speeds found in existing academic tools like Glaze and PhotoGuard. - By implementing these perturbations, any attempts to fine-tune a model on protected work will result in distorted or unintended outputs, effectively shielding the artist's original style. ## Integrated Protection Frameworks - **TOONRADAR**: A comprehensive system deployed since 2017 that uses watermarking for both proactive blocking and retrospective tracking of illegal distributors. - **XPIDER**: An automated detection tool tailored specifically for the comic domain to identify and block harmful UGC, reducing manual inspection overhead. - These solutions are being expanded not just for copyright protection, but to establish long-term trust and reliability in the era of AI-generated content. The deployment of these AI-driven defense mechanisms is essential for maintaining a fair creative economy. By balancing visual quality with robust protection, platforms can empower creators to share their work globally without the constant fear of digital theft or stylistic mimicry.

discord3 min readCurated summary

Modern Image Formats at Discord: Supporting WebP and AVIF

Discord modernized its image pipeline to support animated WebP and AVIF attachments, embeds, and emojis. The change improves animation quality while significantly reducing file sizes compared with GIFs. Discord chose WebP as its primary delivery format because it offers broader platform support and more predictable performance, while converting AVIF sources—including tone-mapping HDR images to SDR when necessary. ## Benefits of WebP and AVIF - **Better transparency:** Both formats support full alpha channels, allowing smooth edges and animations that blend naturally with Discord’s light and dark themes. - **Greater color depth:** - GIF supports only 256 colors. - WebP supports roughly 16 million colors through 8-bit color channels. - AVIF supports up to 12 bits per channel. - **Improved visual quality:** Higher color fidelity reduces banding, produces more accurate colors, and better supports photographic and HDR content. - **Smaller animations:** Modern video-style compression provides: - Inter-frame compression - Advanced prediction - Configurable quality levels - Millisecond-precise frame timing - In the example provided, a GIF was **302 KB**, while the equivalent WebP animation was **43 KB**. ## Cross-Platform Image Processing - Discord processes and resizes every attached or embedded image to provide consistent behavior across: - Web browsers - Windows, macOS, and Linux - iOS - Android - Supporting new formats required coordinating inspection, conversion, decoding, and rendering throughout the media infrastructure. - Discord selected WebP as its preferred transformation target because it offers: - Near-universal platform support - Faster encoding and decoding than AVIF - Mature tools and ecosystem support - More predictable performance ## Tradeoffs Between WebP and AVIF - WebP is less capable than AVIF for: - 10- or 12-bit color - HDR content - Maximum compression efficiency - AVIF can produce smaller files for some content, but its platform support and performance are less consistent. - Discord converts AVIF images to WebP while preserving as much visual quality as possible. - HDR AVIF sources are tone-mapped to SDR because WebP is limited to 8-bit color depth. ## iOS Decoding Challenges - Discord uses SDWebImage to render images on iOS. - Its default WebP implementation relies on Apple’s ImageIO framework, available in iOS 14 and later. - Testing revealed that ImageIO decoded animated WebP files with increasingly inconsistent timing, especially for large or frame-heavy animations. - In a 10-second 480p30 comparison: - `libwebp` decoded the animation in real time. - ImageIO fell more than three seconds behind, with the delay increasing throughout playback. Discord’s move to WebP and AVIF demonstrates how modern formats can deliver higher-quality animations with substantially smaller files. For broad compatibility and reliable playback, WebP serves as the practical default, while AVIF remains useful as a high-efficiency source format.

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figma2 min readCurated summary

Photo Filters Come to Figma | Figma Blog

Figma introduced Image Adjustments in 2017, bringing basic photo-filtering capabilities directly into its browser-based design tool. The feature lets designers modify exposure, contrast, saturation, temperature, tint, highlights, and shadows without exporting images to Photoshop or using workarounds. Figma positioned it as a convenience for quick edits—not a replacement for professional image-editing software. ## Figma’s Return to Photo Editing - Figma began partly as a Photoshop competitor, with early experiments involving browser-based photo filters and masks. - The new feature revisited that original direction in a focused, practical way. ## Image Adjustment Features - Designers can adjust: - Exposure - Contrast - Saturation - Temperature - Tint - Highlights - Shadows - Adjustments are available after placing an image on the canvas. - Users access the filtering controls through the **Fill** section of Figma’s right-hand properties panel. ## Designed for Quick Fixes - The feature eliminates the need to export images to Photoshop for simple changes. - Figma carefully balanced adjustment ranges to avoid undesirable results, such as overly blown-out images at high exposure levels. - The tools are intended to simplify common design tasks rather than provide comprehensive photo-editing capabilities. Figma’s Image Adjustments are best understood as a convenient set of in-product controls for fast visual corrections. Professional photographers and advanced editors will still need dedicated software, but designers can now handle routine image tweaks directly in Figma.

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