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Cross-modal hashing has attracted increasing research attention due to its efficiency for large-scale multimedia retrieval. With simultaneous feature representation and hash function learning, deep cross-modal hashing (DCMH) methods have shown superior performance. However, most existing methods on DCMH adopt binary quantization functions (e.g., sign(·)) to generate hash codes, which limit the retrieval performance since binary quantization functions are sensitive to the variations of numeric values. Toward this end, we propose a novel end-to-end ranking-based hashing framework, in this paper, termed as deep semantic-preserving ordinal hashing (DSPOH), to learn hash functions with deep neural networks by exploring the ranking structure of feature dimensions. In DSPOH, the ordinal representation, which encodes the relative rank ordering of feature dimensions, is explored to generate hash codes …