A Survey On Recent Research Trends Towards Compression Of Image Using DPCM

Abstract: Efficient image compression is essential for minimizing storage requirements and reducing transmission bandwidth while preserving the visual quality of reconstructed images. Among the various compression techniques there is a Differential Pulse Code Modulation (DPCM) represents an effective predictive compression approach has gained considerable attention so because of its predictive coding mechanism, which utilizes the spatial dependency among the adjacent pixels by encoding prediction errors rather than absolute pixel values. This approach effectively reduces data redundancy by maintaining low computational complexity, making it well suited for real-time image computing applications.
This paper presents a detailed review of DPCM-based image compression methods, including their operating principles, prediction techniques, hardware architectures, and performance evaluation. It also discusses recent improvements in adaptive and hybrid DPCM approaches that enhance compression efficiency and reconstructed image quality. Furthermore, the study compares existing research based on compression performance, hardware resource utilization, implementation complexity, and processing speed. Finally, current research challenges, emerging trends, and future opportunities involving intelligent prediction methods, machine learning, and next-generation image coding techniques are highlighted. The review concludes that DPCM continues to be a practical, efficient, and hardware-friendly approach for real-time image compression applications.

Keywords: Differential Pulse Code Modulation (DPCM), comparative analysis, Image compression, Wavelet Transform, Biomedical Signal Processing, Real-Time Signal Processing, Sustainable Healthcare, Wearable Healthcare.