Image compression is a fundamental component of digital communication and storage. General-purpose codecs such as JPEG, PNG, and WebP are optimized for average performance across diverse images, while neural-network-based approaches can improve compression ratios but often incur high computational cost and low throughput, limiting their practical use. Domain-specific compression, which exploits repeated patterns and redundancies in homogeneous datasets, offers an attractive alternative. This paper presents an evolutionary framework for automatically designing lossless image codecs specialized for domain-specific data, such as static-camera footage, industrial scans, or satellite imagery. The framework co-evolves pixel scan orders and predictors to minimize residual entropy, achieving a balance between compression efficiency and throughput. Experiments on astronomical, medical, natural, and synthetic image datasets demonstrate that the evolved codecs can outperform baseline and standard codecs in bits per pixel and processing speed, highlighting their potential for fast, adaptive, and interpretable compression in embedded and edge computing applications.