Resource-Optimized Quantum Algorithms for Real-Time Video Coding

Abstract

 Under stringent latency and computational constraints, real-time video coding must quickly divide the coding unit, carry out motion estimation, rate control, and mode selection. Conventional optimization techniques improve compression efficiency, but when dealing with high-resolution video, motion, and coding structure, their search complexity is comparatively large. A Quantum Algorithm for Real-Time Video Coding with Resource Optimization is Presented. Utilize a quantum-assisted search method to optimize mode selection, bit allocation, and compute scheduling by modeling the coding choice process as a constrained combinatorial optimization problem. To optimize the trade-off between reconstruction quality, bitrate, encoding delay, and hardware use, a Resource-Aware Control Module will be included. The suggested approach keeps the PSNR loss within 0.18 dB of exhaustive rate-distortion optimization while reducing the average encoding complexity by 27.4% and the decision latency by 22.8%, according to experiments on high-definition video sequences. Thus, it can be said that a good algorithm for low-latency video coding and future quantum-inspired multimedia computing is quantum-assisted resource optimization.