A Review of Trends and Developments in Document Image Compression Methods from 2016 to 2025 - Neni Nur Laili Ersela Zain (Fakultas Ilmu Pendidikan Universitas Pahlawan Tuanku Tambusai, 2025)
This study aimed to analyze and classify research developments in document image compression from 2016 to 2025, identifying prevailing methods, technical characteristics, and chronological trends. A total of 56 journal articles were retrieved from the ScienceDirect database using the keywords “document image compression,” “text image compression,” and related terms, limited to English language publications in Computer Science, Signal Processing, and Image Processing. The selection followed a three stage process identification, relevance screening, and methodological classification resulting in six papers directly focused on document image compression. The results revealed three main phases of development: classical transform methods (2016-2018) such as JPEG2000, SPIHT, Chain Code, JBIG, JBIG2, and JPEG-LS; hybrid enhancement compression approaches (2017-2019) including DjVu and PDE collaborative models; and learning frameworks (2019-2025) featuring Autoencoder, VAE, GAN, Transformer, and JPEG AI. Overall, the field has evolved from traditional mathematical transforms to adaptive, data models. While research specifically targeting document images remains limited, emerging neural and hybrid methods highlight growing attention to readability preservation, OCR compatibility, and efficient digital archiving.