Underwater communication is constrained by limited bandwidth, high latency, and signal attenuation, particularly in acoustic transmission scenarios. To address these challenges, this paper investigates a simulation-based framework for underwater image transmission using discrete cosine transform (DCT) and quantization-based compression. Grayscale images are compressed at varying quantization levels (3-bit to 8-bit) and transmitted over a simulated underwater acoustic channel with relay-based attenuation and noise. The impact of compression on image fidelity is evaluated using Peak Signal-to-Noise Ratio and Structural Similarity Index (PSNR, SSIM), while object detection robustness is assessed via classification accuracy using a MobileNetV2 model. Experimental results demonstrate a clear trade-off between compression efficiency and detection accuracy, offering insights into optimal quantization strategies for bandwidth-limited underwater environments. This simulation framework, developed entirely in MATLAB, provides a scalable testbed for evaluating vision-centric Internet of Underwater Things (IoUT) applications prior to real-world deployment on resource-constrained devices.