Journal of Multimedia Processing and Technologies Martin Lopez Nores 17 3 2026 https://doi.org/10.6025/jmpt/2026/17/3/152-176 https://www.dline.info/jmpt/fulltext/v17n3/jmptv17n3_3.pdf Autoencoders are foundational unsupervised neural networks that learn compact data representations by minimizing reconstruction error. This study presents a comprehensive quantitative and qualitative evaluation of a convolutional autoencoder (CAE) for image reconstruction using the CIFAR-10 dataset. The model, comprising approximately 1.05 million trainable parameters, compresses 32×32×3 RGB images into a 64- dimensional latent space before reconstructing them. A multidimensional evaluation framework integrates pixel-level metrics (MAE, MSE, RMSE), signal quality (PSNR), and perceptual similarity (SSIM), supplemented by diagnostic visualizations including side by side comparisons, error heatmaps, per-image error distributions, and class wise performance breakdowns. The autoencoder achieved a PSNR of 24.88 dB and SSIM of 0.786, indicating acceptable signal quality and good structural preservation. Statistical validation using one-way ANOVA (F(9,9990) = 17.84, p < 0.001) and Tukey HSD post hoc tests revealed significant class-dependent reconstruction differences, with vehicle categories (automobile, truck, ship) exhibiting lower error than animal classes (cat, dog, bird), reflecting the influence of semantic complexity on compression efficacy. Crucially, latent space analysis through PCA, t-SNE, and UMAP visualizations, alongside clustering metrics (Silhouette Score = 0.74, Calinski Harabasz Index = 4256.9), demonstrated that semantically meaningful class organization emerges without explicit supervision, with confusion heatmaps showing 89- 95% diagonal alignment with ground truth labels. This study contributes a robust, reproducible benchmarking framework for autoencoder evaluation, establishes the relationship between reconstruction fidelity and latent space semantic structure, and positions the CAE as an effective feature extractor for downstream tasks such as unsupervised classification, anomaly detection, and transfer learning in computer vision pipelines.