lossiness

Lossiness refers to the property of a process that discards part of the original information, resulting in irreversible changes to the data. In data compression and signal processing, lossy methods reduce data size by removing information deemed redundant or perceptually insignificant. By contrast, lossless methods preserve all original data and allow exact reconstruction.

Lossy compression algorithms, such as JPEG for images, MP3 for audio, and MPEG for video, remove information based on models of human perception and statistical redundancy. The degree of lossiness is controlled by parameters like quantization step size, quality settings, or target bit rate. Higher compression typically increases lossy artifacts and lowers fidelity.

Consequences of lossiness include smaller file sizes and reduced bandwidth requirements, but at the cost of irretrievable information loss and potential artifacts such as blockiness, blurring, ringing, or color shifts. The acceptability of lossiness depends on the use case and the perceptual importance of discarded data. In many systems, lossy compression is used in combination with error concealment or enhancement techniques to mitigate visible or audible artifacts.

Measurement and evaluation of lossy systems often involve objective metrics and subjective testing. Objective measures such as PSNR or SSIM for images, and other perceptual metrics for audio and video, quantify reconstruction quality, while subjective listening or viewing tests assess perceived quality. Perceptual models aim to maximize perceived quality at a given bitrate.

Applications of lossy methods span streaming media, storage optimization, bandwidth-constrained transmission, and real-time communication, where some information loss is acceptable in exchange for efficiency.