对比9种Python JPEG解码库，实测性能差异最大达1.5倍。
Need for Speed: A Comprehensive Benchmark of JPEG Decoders in Python
- 在ARM64与x86_64架构上测试多种解码库性能
- libjpeg-turbo实现最高1.5倍于传统方法的解码速度
- 为训练与实时应用提供选型建议
图像加载是现代机器学习流程中的关键瓶颈，尤其在计算机视觉任务中，JPEG仍是主流格式。本研究系统分析了九种流行Python JPEG解码库在不同计算架构上的性能表现。测试涵盖传统图像处理库（Pillow、OpenCV）、机器学习框架（TensorFlow、PyTorch）及专用解码器（jpeg4py、kornia-rs），平台包括ARM64（Apple M4 Max）与x86_64（AMD Threadripper）。结果表明，采用libjpeg-turbo的现代实现相比传统方法解码速度提升最高达1.5倍。研究基于实测数据，为不同场景（如高吞吐量训练管道与实时应用）提供优化选型建议，助力从业者提升图像加载基础设施效率，缩短训练时间，增强系统性能。
原文摘要 · Abstract (English)
Image loading represents a critical bottleneck in modern machine learning pipelines, particularly in computer vision tasks where JPEG remains the dominant format. This study presents a systematic performance analysis of nine popular Python JPEG decoding libraries on different computing architectures. We benchmark traditional image processing libraries (Pillow, OpenCV), machine learning frameworks (TensorFlow, PyTorch), and specialized decoders (jpeg4py, kornia-rs) on both ARM64 (Apple M4 Max) and x86\_64 (AMD Threadripper) platforms. Our findings reveal that modern implementations using libjpeg-turbo achieve up to 1.5x faster decoding speeds compared to traditional approaches. We provide evidence-based recommendations for choosing optimal JPEG decoders across different scenarios, from high-throughput training pipelines to real-time applications. This comprehensive analysis helps practitioners make informed decisions about image loading infrastructure, potentially reducing training times and improving system efficiency.
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