A Deep CNN-Based Approach For Joint Image Compression And Segmentation

Abstract

The rapid proliferation of high-resolution imaging devices across medical diagnostics, remote sensing, autonomous systems, and multimedia platforms has intensified the demand for efficient storage and intelligent interpretation of visual data. Conventional image compression and segmentation techniques are typically addressed as independent tasks: compression focuses on reducing redundancy for storage and transmission efficiency, while segmentation aims to extract semantically meaningful regions for analysis. However, separating these processes often results in redundant computations, suboptimal feature utilization, and potential degradation of structural information critical for downstream vision tasks. This study proposes a unified deep convolutional neural network (CNN)-based framework designed to perform joint image compression and segmentation within a single end-to-end architecture. The proposed approach integrates an encoder–decoder compression backbone with a parallel segmentation head that shares intermediate feature representations. The encoder learns compact latent embeddings that preserve both perceptual fidelity and semantic relevance, while the decoder reconstructs compressed images with minimal distortion. Simultaneously, the segmentation branch leverages multi-scale feature maps extracted during encoding to generate accurate pixel-wise classification maps. A hybrid loss function is formulated to balance rate–distortion optimization with segmentation accuracy, ensuring that compression efficiency does not compromise structural boundary preservation. To enhance robustness, attention modules and residual connections are incorporated to retain fine-grained spatial details, particularly in high-frequency regions that are typically vulnerable to compression artifacts. Extensive experimentation is conducted on benchmark datasets representing natural and domain-specific images to evaluate reconstruction quality, compression ratio, and segmentation performance. The results demonstrate that the joint framework achieves competitive compression rates while maintaining superior segmentation accuracy compared to traditional sequential pipelines. Quantitative metrics such as peak signal-to-noise ratio, structural similarity index, and mean intersection-over-union confirm that shared feature learning improves both tasks simultaneously. Furthermore, the integrated architecture reduces computational redundancy and inference time, making it suitable for real-time and resource-constrained environments such as edge devices and embedded systems. The findings highlight the potential of multi-task deep learning models to bridge the gap between data efficiency and semantic understanding. By embedding segmentation awareness into the compression process, the proposed CNN-based approach not only preserves visually significant content but also enhances task-oriented image representation. This research contributes to the advancement of intelligent visual processing systems, offering a scalable and adaptive solution for applications requiring simultaneous compression and analytical interpretation.