Hashing has been widely used for large-scale multimedia retrieval because of its advantages in storage and retrieval efficiency. Traditional supervised hash methods represent an image as a feature vector and then perform a separate quantization step to generate a binary code. Due to the difficulty of discrete optimization of hash codes, continuous relaxation is generally used to replace discrete optimization. However, the process of continuous relaxation leads to inevitable quantization error. To avoid this drawback, a deep balanced discrete hashing method is proposed, which uses discrete gradient propagation with the straight-through estimator. The proposed method does not use the traditional continuous relaxation strategy, thereby reducing the quantization error caused by continuous relaxation. And the proposed method uses supervised information to directly guide the discrete coding and deep feature learning process. In the proposed method, the last layer of the Convolutional Neural Network (CNN) outputs the binary code directly. In the loss function, discrete values are calculated by combining the pairwise loss and a balance controlling term. The learned binary hash code maintains the similar relationship and label consistency at the same time. While maintaining the pairwise similarity, the proposed method keeps the balance of hash codes to improve retrieval performance. Extensive experiments show that the proposed method outperforms the state-of-the-art hashing methods on four image retrieval benchmark datasets. © 2020 Elsevier B.V.