摘要： Recent years, real time videos are playing an important role in different applications such as pattern recognition, security purpose, news analysis, weather digest, and video browser and so on. Due to the importance of real time video applications, the quality of the real time video must be improved for making effective results in real time video analysis process. This paper introduces the particle optimization with adaptive cumulative distribution based histogram enhancement technique (PACDHE) for improving the real time video quality. Initially the videos are collected, each incoming frame has been analyzed and noise present in the video frame is eliminated by applying the non-divisional median filter. After that, quality of real time video is enhanced iteratively by examining each pixel present in video frames using the optimized fitness and cumulative distribution function. This process is repeated continuously until to enhance the real time video frames contrast and quality. Then the performance of the system is analyzed by using CV online video database and the efficiency is examined in terms of peak signal to noise ratio (PSNR), Absolute Mean Brightness Error (AMBE) and Entropy. The experimental results of PSO are compared with genetic algorithm based approach and found that PSO outperforms the GA approach and the existing histogram equalization approach and the existing histogram equalization approaches.