Achieving a balance between security and retrieval accuracy presents a significant challenge in secure content-based image retrieval (SCBIR), particularly in untrusted cloud environments. Conventional SCBIR schemes often struggle with trade-offs, either compromising data privacy, reducing retrieval performance, or introducing significant computational overhead, making them unsuitable for large-scale real-world applications. To overcome these challenges, we propose SCBIR-PE, a novel SCBIR scheme that integrates block-based perceptual encryption with an efficient histogram-based feature extraction method. The encryption incorporates geometric and color transformations at block and sub-block levels across three distinct extensions, confirming security robustness while preserving image features necessary for an image retrieval application. Thus, feature extraction is performed using the Color and Edge Directivity Descriptor (CEDD), enabling direct feature computation in the encrypted domain. Additionally, Euclidean distance is utilized as a similarity measurement technique, ensuring efficient image retrieval while avoiding computationally intensive tasks like clustering, index generation, or feature mapping, which are at the core of existing SCBIR schemes. Experimental evaluations demonstrate that SCBIR-PE scheme outperforms conventional privacy preserving image retrieval schemes in terms of retrieval accuracy (up to 4% better mean average precision score), security robustness, and computational efficiency. By ensuring strong privacy protection with minimal computational overhead, our proposed scheme offers a scalable and efficient solution for cloud-assisted secure image retrieval applications.