A parallel model for intra prediction in hevc on heterogeneous CPU+GPU platforms

As the importance of streaming services and video traffic continues to grow, along with their significant popularity in global internet traffic, there is an ongoing struggle to cope with limited bandwidth while presenting high-resolution videos. This situation has underscored the need for encoding techniques that enable more efficient compression without compromising video quality. HEVC (High-Efficiency Video Coding) stands out as a new coding standard capable of achieving more efficient compression compared to its predecessors. In this study, the core steps of the HEVC standard, intra prediction, transform, quantization, inverse quantization, and inverse transform were executed in parallel. Parallelization was proposed and assessed during the mode decision process in the intra prediction step. Furthermore, the application of the Discrete Cosine Transformation (DCT) during the transformation step was explored using two different methods in parallel on a GPU, with a focus on performance efficiency. All suggested methods were efficiently implemented on the GPU using shared memory. The proposed methods were evaluated using the Peak Signal-to-Noise Ratio (PSNR) criterion and compression ratio. Test images were used to calculate execution times based on the quantization parameter (QP) for all methods. These times were compared for all methods, and their impacts were analyzed. Test results indicate that the proposed methods effectively deliver efficient performance on the image without compromising quality.