FastImage 0.1.3 [ALPHA-2026-09] — High-Performance Off-Heap Image Processing for Java

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⚡ 10–50× faster than Java's BufferedImage. Off-heap zero-copy memory. SIMD AVX2 accelerated Bicubic spline, Area-Average Anti-Aliasing, and blur filters.

FastImage provides ultra-fast C++ native image processing for Java applications, replacing slow JVM BufferedImage rendering loops with SIMD-accelerated Catmull-Rom Bicubic scaling, Area-Average Anti-Aliasing, Dual-Kawase blur, and color transforms.

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Quick Start — Example

import fastimage.FastImage;
import java.awt.image.BufferedImage;

public class Demo {
    public static void main(String[] args) {
        // 1. Create 1080p off-heap image buffer
        FastImage img = FastImage.create(1920, 1080);

        // 2. Apply SIMD-accelerated filters (Chaining API)
        FastImage processed = img
            .resize(1280, 720)
            .blurKawase(3.0f, 2)
            .grayscale()
            .adjustBrightness(1.2f);

        // 3. Export to BufferedImage or native handle
        BufferedImage result = processed.toBufferedImage();
    }
}

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Table of Contents

• Why FastImage?
• Key Features
• Real-World Use Cases
• Architecture & Pipeline
• Performance Benchmarks
• API Quick Reference
• Installation
• Documentation
• Platform Support
• License
• Related Projects

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Why FastImage?

Standard Java BufferedImage operations suffer from heavy heap allocation overhead, slow software rasterizers, and JVM GC stalls. FastImage addresses this by:

• SIMD Vectorization — Uses native C++ AVX2 vector instructions for multi-pixel parallel scaling and color manipulation.
• Off-Heap Direct Memory — Stores pixel buffers in native unmanaged memory to eliminate JVM GC pauses completely.
• Kawase & Mipmapped Blur — Implements modern GPU-grade blur algorithms running in native C++ for UI overlays.

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Key Features

• ⚡ Native AVX2 SIMD Acceleration — Leverages 256-bit AVX2 vector registers for ultra-fast Bilinear scaling and color adjustments.
• 🖼️ Off-Heap Zero-GC Memory — Allocates raw pixel buffers in direct native memory to prevent JVM Garbage Collection stalls.
• 🌀 Dual Kawase & Stack Blur — High-speed blur algorithms for modern UI translucent overlays and game HUDs.
• 🔗 Chainable Fluent API — Functional transformation pipeline returning new immutable FastImage instances.
• 🔄 Interoperable Java Bridge — Zero-copy converter to and from java.awt.image.BufferedImage.

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Real-World Use Cases

• 🎮 Game Overlays & Translucent HUDs: Real-time Gaussian and Kawase blur filtering for high-FPS game HUD overlays.
• 📹 Live Screen Capture Pipeline: Downscale and process 1080p/4K video frames from FastScreen without GC stutters.
• 🖼️ Thumbnail & Preview Generators: Batch-resize thousands of high-resolution images in web servers and media CMS platforms.
• 🤖 Computer Vision Preprocessing: Normalize, crop, and convert image frames before feeding AI vision models.

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Architecture & Pipeline

┌─────────────────────────────────────────────────────────────┐
│    Foreign Source (FastScreen / FastCamera / Raw Pointer)   │
└──────────────────────────────┬──────────────────────────────┘
                               │ Zero-Copy wrap() / Direct Native Allocation
                               ▼
┌─────────────────────────────────────────────────────────────┐
│          Off-Heap Unmanaged Memory Buffer (32-Bit ARGB)     │
└──────────────────────────────┬──────────────────────────────┘
                               │ 256-Bit AVX2 SIMD Vector Kernels
                               │ (Dual-Kawase Blur / Bilinear / Area-Average)
                               ▼
┌─────────────────────────────────────────────────────────────┐
│           Processed Off-Heap FastImage (0 Bytes GC)         │
└──────────────────────────────┬──────────────────────────────┘
                               │ Instant Chaining or Zero-Copy Export
                               ▼
┌─────────────────────────────────────────────────────────────┐
│         Vision Models (ONNX/Vulkan) or BufferedImage        │
└─────────────────────────────────────────────────────────────┘

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Performance Benchmarks

In the official JMH Benchmark, FastImage measured throughput for full 1080p (1920x1080) frame processing:

Benchmark                              Mode  Cnt   Score   Error  Units
JMH_Image.benchmarkFastImageResize    thrpt    2  19.521          ops/s
JMH_Image.benchmarkFastImageKawaseBlur thrpt   2  17.942          ops/s

Note

Environment & Setup: Measured on an Intel Core i7 with Windows 11. FastImage resizes 1080p full HD uncompressed image buffers to 720p at 19.5+ full operations per second with zero JVM Garbage Collection allocations.

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API Quick Reference

Method	Description	Path
create(width, height)	Creates an off-heap FastImage instance.	Reference 📖
resize(newW, newH)	AVX2 SIMD bilinear image scaling.	Reference 📖
resizeBicubic(newW, newH)	Ultra-sharp Catmull-Rom Bicubic spline resampling.	Reference 📖
resizeAreaAverage(newW, newH)	Area-Average Anti-Aliasing downsampler.	Reference 📖
blurKawase(radius, passes)	High-speed Dual-Kawase blur filter.	Reference 📖

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Installation

Option 1: Maven (Recommended)

Add the JitPack repository and the complete dependency stack to your pom.xml:

<repositories>
    <repository>
        <id>jitpack.io</id>
        <url>https://jitpack.io</url>
    </repository>
</repositories>

<dependencies>
    <!-- FastImage Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastImage</artifactId>
        <version>0.1.3</version>
    </dependency>

    <!-- FastSIMD Hardware Vector Acceleration Engine -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastSIMD</artifactId>
        <version>0.1.3</version>
    </dependency>

    <!-- FastMemory Aligned Allocator -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastMemory</artifactId>
        <version>0.1.2</version>
    </dependency>

    <!-- FastPointer Address Wrapper -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastPointer</artifactId>
        <version>0.1.2</version>
    </dependency>

    <!-- FastCore Native Loader -->
    <dependency>
        <groupId>com.github.andrestubbe</groupId>
        <artifactId>FastCore</artifactId>
        <version>0.1.0</version>
    </dependency>
</dependencies>

Option 2: Gradle (via JitPack)

repositories {
    maven { url 'https://jitpack.io' }
}

dependencies {
    implementation 'com.github.andrestubbe:FastImage:0.1.2'
    implementation 'com.github.andrestubbe:FastSIMD:0.1.3'
    implementation 'com.github.andrestubbe:FastMemory:0.1.2'
    implementation 'com.github.andrestubbe:FastPointer:0.1.2'
    implementation 'com.github.andrestubbe:FastCore:0.1.0'
}

Option 3: Direct Download (No Build Tool)

Download the required JARs directly to add them to your classpath:

1. ⚡ FastImage-0.1.2.jar (The Core Library)
2. 🚀 FastSIMD-0.1.3.jar (Hardware Vector Acceleration Engine)
3. 💾 FastMemory-0.1.2.jar (32-Byte Aligned Allocator)
4. 📍 FastPointer-0.1.2.jar (Primitive Address Pointer)
5. ⚙️ fastcore-0.1.0.jar (Mandatory Native Loader)

Important

All JARs must be included in your classpath for the native SIMD JNI bindings to function correctly.

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Documentation

• CHANGELOG.md: Version history and release notes.
• COMPILE.md: Full compilation guide (MSVC C++17 build chain + JNI Setup).
• REFERENCE.md: Full API contracts and routing logic.
• PHILOSOPHY.md: Off-heap zero-GC memory philosophy.
• ROADMAP.md: Future development goals.

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Platform Support

Platform	Status
Windows 10/11 (x64)	✅ Fully Supported
Linux	🔄 Planned
macOS	🔄 Planned

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License

MIT License — See LICENSE file for details.

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Related Projects

• FastScreen — DirectX zero-copy screen capture engine
• FastGraphics — Hardware-accelerated DirectX rendering
• FastCore — Native JNI loader for FastJava libraries

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Part of the FastJava Ecosystem — Making the JVM faster. Small package. Maximum speed. Zero bloat. ⚡