Image and Audio Processing

Image and audio processing deal with transforming physical signals into digital representations and extracting useful information from them. Cameras produce spatial samples, while microphones produce time-dependent samples. Their data structures differ, yet both domains share core ideas such as sampling, quantisation, noise, filtering, spectral analysis and feature extraction.

Digital signal chain

A processing pipeline may include sensing, analogue front-end circuitry, digitisation, preprocessing, representation, modelling and decision stages. Input quality constrains every stage that follows, so model accuracy alone does not describe system quality.

The sampling theorem defines the limits under which continuous signals can be represented by discrete samples. The Fourier transform provides a frequency-domain view of the signal. Edges and texture in images, and formants or spectral energy patterns in audio, can become easier to analyse through these representations.

Classical and learned methods

Classical techniques rely on explicit operations such as filtering, thresholding, morphology, correlation and geometric transforms. Learned systems derive representations and decision boundaries from data. Practical systems often combine both. An image model may depend on resize and normalisation stages, while a speech model may receive VAD-filtered log-Mel features.

System metrics

Accuracy should be considered together with latency, throughput, memory use, model size, energy consumption and data movement. Offline forensic analysis and interactive real-time processing have different performance envelopes.

Multimodal learning combines image, audio and text through shared or related representations. It becomes valuable when one sensor or modality contains incomplete evidence.

Image and audio processing are therefore end-to-end information-processing problems extending from physical acquisition to the final decision.