The review organizes the literature into a three-stage taxonomy that spans classical methods, deep learning approaches, and hybrid or multimodal systems. Classical anomaly detection relied on statistical modeling or reconstruction-based learning, where a system learns what normal data looks like and flags anything it cannot reconstruct or fit well. Deep architectures transformed the field into a data-driven enterprise able to model intricate spatial and temporal dynamics, capturing how objects and motions unfold across time in video.

The most recent wave combines multiple paradigms: diffusion-transformer hybrids, federated compressed-learning frameworks, and multimodal vision-language reasoning that lets systems reason about anomalies using both visual and textual cues. By placing these generations side by side, the review offers an interdisciplinary synthesis of how the field's core ideas have evolved rather than treating each technique in isolation. The review's most distinctive contribution is its focus on compressed-domain anomaly detection, an approach the authors describe as novel.

Instead of fully decoding a video and then analyzing its frames, compressed-domain methods work directly on the encoded stream itself, exploiting motion vectors, discrete cosine transform coefficients, and residual data that already exist inside compressed video. According to the review, this design enables real-time analytics without complete decoding and offers a privacy-preserving advantage, since the raw pixel content is never fully reconstructed. The authors do not present this as a solved problem.

The review critically assesses the trade-offs between accuracy, latency, scalability, and privacy that every anomaly detection system must navigate, and it highlights ongoing difficulties in codec generalization and distributed robustness. The review identifies both as ongoing difficulties for the field. Beyond cataloguing methods, the review integrates research across three visual domains, still images, video, and compressed video streams, and proposes a codec-agnostic perspective that treats the underlying compression format as less important than the anomaly signals themselves.

It closes with a roadmap for building anomaly detection systems that are efficient, scalable, and ethically aligned, tailored for real-world intelligent multimedia environments where privacy and real-time constraints matter as much as detection accuracy. As a review article, the work synthesizes and evaluates existing literature rather than reporting new experimental results, but its mapping of the field, and its argument that compressed-domain analysis deserves a central place in future research, gives researchers and practitioners a consolidated view of where anomaly detection stands and where it is heading.

Subject of Research: A review of anomaly detection methods for images, videos, and compressed video streams

Article Title: A comprehensive review of anomaly detection: from images to videos and compressed video streams

Article References: Manzoor, N., & Bhat, J. I. (2026). A comprehensive review of anomaly detection: from images to videos and compressed video streams. Knowledge and Information Systems, 68(1), Article 296. https://doi.org/10.1007/s10115-026-02923-x

Image Credits: AI Generated

DOI: 10.1007/s10115-026-02923-x

Keywords: anomaly detection, computer vision, deep learning, compressed video, motion vectors, discrete cosine transform, diffusion models, federated learning, multimodal reasoning, privacy-preserving analytics, Knowledge and Information Systems, comprehensive