A Comparative Analysis of Machine Learning based Colour Image Compression Mechanisms

These instructions give you basic guidelines for preparing camera-ready papers for Hinweis Research conference proceedings/Journal Publications. Optimizing image size plays a pivotal role in managing multimedia content effectively, facilitating efficient storage and transmission of visual information. Conventional methods such as JPEG have historically set the benchmark, yet the emergence of machine learning (ML) has catalyzed the exploration of innovative avenues in image compression techniques. This paper presents a comparative analysis of machine learning-based color image compression mechanisms, evaluating their performance, efficiency, and applicability. The study begins with an overview of traditional image compression techniques and introduces machine learning's role in revolutionizing this field. Various ML-based compression approaches, including autoencoder-based methods, generative adversarial networks (GANs), and neural network-based methods, are discussed in detail, highlighting their working principles and key characteristics. Metrics like Peak Signalto- Noise Ratio (PSNR), Structural Similarity Index (SSIM), Compression Ratio (CR), and computational complexity serve as benchmarks to assess the effectiveness of each compression technique. Datasets and experimental setups are carefully selected to ensure a comprehensive analysis. Results and discussions reveal the strengths and weaknesses of each compression mechanism, shedding light on the trade-offs between compression ratio and image quality, as well as computational efficiency. Real-world applications and use cases demonstrate the practical relevance of these techniques in various domains. Challenges and future directions are identified, paving the way for further research and improvements in ML-based image compression. In conclusion, this comparative analysis offers valuable insights for selecting appropriate compression techniques based on specific requirements and constraints, contributing to the advancement of image compression technology in the era of machine learning.