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Table of contents

• What is image processing?
• What is video processing?
• Key differences between image and video analysis
• Why does it matter?
• Use cases for image and video processing
• Image & video pipelines in modern BaaS platforms
• FAQs

What is image processing?

Image processing is the analysis of static visual data. Each image is treated as a single frame, allowing models to classify objects, detect anomalies, or segment regions of interest.

Typical techniques include:

• Classification: Assigning a label (e.g., cat vs. dog).
• Detection: Drawing bounding boxes around objects.
• Segmentation: Outlining precise object boundaries.
• Enhancement: Adjusting brightness, contrast, or denoising.

Because images are self-contained, image processing is generally faster and less resource-intensive than video analysis.

What is video processing?

Video processing analyzes sequences of frames over time, introducing the concept of motion and temporal relationships.

Instead of a single snapshot, video models must track objects, detect events, and understand context across multiple frames.

Key operations include:

• Object tracking: Following an item across frames.
• Action recognition: Identifying behaviors (e.g., “person running”).
• Event detection: Spotting anomalies like falls, collisions, or defects.
• Temporal segmentation: Breaking videos into meaningful segments.

This makes video processing more computationally demanding but also richer in insight.

Key differences between image and video analysis

• Data size: Videos generate exponentially more data.
• Complexity: Video requires handling both spatial and temporal information.
• Latency: Real-time video inference needs optimized pipelines.
• Use cases: Images excel in static inspection; videos shine in monitoring and behavior analysis.

Why does it matter?

Choosing between image and video processing affects:

• Infrastructure costs (storage, bandwidth, compute).
• Model architecture (CNNs for images vs. 3D CNNs/transformers for video).
• Deployment strategy (batch uploads vs. real-time streams).

Understanding the trade-offs ensures your backend is both cost-efficient and effective.

Use cases for image and video processing

• Image: Medical scans, quality inspection, product labeling.
• Video: Security monitoring, sports analytics, driver assistance.
• Hybrid: Retail stores analyzing both still shelf images and real-time shopper movement.

Image & video pipelines in modern BaaS platforms

With platforms like Lid Vizion, developers don’t need to reinvent the backend. A BaaS workflow can:

• Trigger serverless jobs when an image is uploaded.
• Stream video through inference APIs for real-time detection.
• Store both frames and embeddings for search and retrieval.
• Scale automatically with demand, whether batch uploads or live feeds.

This simplifies building intelligent applications that handle both static and dynamic visual data.

FAQs

Can image models be applied to video?
Yes. Many video pipelines process each frame with an image model, but dedicated video models capture motion better.

Is video processing always more accurate?
Not always. For certain tasks (like document OCR), images are enough. Video adds context when actions or sequences matter.

Do I need GPUs for video analysis?
For real-time or high-resolution video, yes. For lightweight offline tasks, CPUs may suffice.

How do I store video data efficiently?
Most pipelines store compressed streams (MP4, H.264) and extract frames as needed, saving embeddings instead of raw frames for faster search.

Can image and video pipelines run together?
Absolutely. Many apps combine both — analyzing still photos for classification and live video for real-time monitoring.

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