ABSTRACT Blosc2 is a high‐performance compression library and data format designed for binary data such as numerical arrays, tensors, and other structured types. In this proposal, we aim to develop two new codecs for Blosc2 by leveraging its extensible plugin‐based codec framework. Specifically, we propose integrating archetypal analysis (AA) and fuzzy clustering as novel codecs within Blosc2. Our proposal has been assessed by using the Olivetti faces dataset and results have demonstrated that AA outperforms fuzzy clustering in preserving fine‐grained data details, achieving substantially higher Structural Similarity Index. These results underscore AA's capability to capture the complete data distribution, including its extreme values, which is an essential property for achieving high‐fidelity compression.