Design and Analysis of Image Compression Using Autoencoders

This paper aims to design and analyze an image compression technique using an autoencoder-based deep learning model. Traditional image compression methods, such as JPEG and PNG, rely on transform coding techniques such as Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT), which may not successfully capture complicated spatial redundancy. In contrast, autoencoders can learn compact latent representations of images, enabling efficient compression while preserving essential details. The proposed model consists of a convolutional encoder that extracts hierarchical features and reduces dimensionality, followed by a decoder that reconstructs the image from the compressed representation. The model's performance is assessed using important metrics such as compression ratio (CR), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean squared error (MSE). Experimental results demonstrate that the autoencoder-based compression technique achieves competitive or superior performance compared to conventional methods, particularly at high compression rates. Furthermore, we analyze the computational efficiency in terms of encoding and decoding time, assessing its feasibility for real-time applications. This work demonstrates the potential of deep learning for developing image compression algorithms and gives insights into the trade-offs between compression efficiency and picture quality.