Compressed Domain

Compressed domain processing aims to perform computations directly on compressed data, bypassing the computationally expensive step of decompression, thereby improving efficiency and reducing latency. Current research focuses on developing lossy compression methods that allow for fundamental operations (e.g., matrix manipulations, image classification) directly on compressed representations, utilizing techniques like diffusion models, vector quantization, and specialized neural network architectures adapted for frequency-domain data. This approach holds significant promise for accelerating various applications, including image and video processing, computer vision tasks, and biomedical signal analysis, particularly in resource-constrained environments like mobile devices and embedded systems.