Intelligent Fractal Video Compression and Super-Resolution Zooming

Fractal image ompression (FIC) provides the distinctive advantage of resolution independence, enabling deep zooming and super-resolution without the pixelation artifacts commonly observed in traditional Discrete Cosine Transform (DCT)-based methods. Despite these benefits, the high computational cost of exhaustive domain block searching has limited the practical adoption of FIC for real-time video applications. In this paper, we propose a novel high-performance architecture for fractal video compression. The proposed approach incorporates a variance-based intelligent search heuristic to substantially reduce the domain search space, along with a massively parallel GPU kernel for efficient affine block matching. According to experimental findings, the suggested approach maintains a high structural similarity index measure (SSIM) and supports resolution-independent zooming capabilities while achieving notable performance improvements over CPU-based implementations.

Despite its theoretical benefits, the computational difficulty of FIC has severely limited its practical usage, especially for video compression. To find the optimal affine match for each range block, the encoding method necessitates a thorough search across a vast pool of domain blocks. The computational cost of this brute-force matching is on the order of O(N_r x N_d), where N_R and N_D represent the number of range and domain blocks, respectively. In conventional CPU-based systems, this complexity makes real-time encoding unfeasible. To overcome this fundamental bottleneck, this paper proposes a high-performance fractal video compression framework that leverages both algorithmic optimization and hardware acceleration.

By combining intelligent search heuristics with GPU parallelism, the proposed approach substantially accelerates fractal encoding while maintaining high reconstruction quality and preserving the intrinsic resolution-independent benefits of fractal compression.

The study demonstrated the successful implementation and testing of a CUDA-Accelerated Intelligent Fractal Video Compressor that resolves the traditional performance limitations of fractal image compression (FIC). Our key achievement came from combining an intelligent variance-based heuristic with a parallel GPU kernel that we optimized highly.