Scrambling-Driven Optimization Framework for H.264 Video Compression

Abstract

This paper presents a framework for enhancing the H.264 video compression standard by adding scrambling techniques and Run Length Encoding (RLE) to the encoding process. The new approach adds XOR-based scrambling, the Arnold Transform, and pixel swapping following quantization and zigzag scanning to enhance data dispersion and structure transformation prior to entropy coding. Rather than applying traditional Huffman coding, Context-Based Adaptive Binary Arithmetic Coding (CABAC) is employed to enhance compression efficiency. The framework was simulated using OpenCV, NumPy, and Matplotlib. Experimental results indicate that the enhanced H.264 encoder achieves a 7% improvement in the average Peak Signal-to-Noise Ratio (PSNR) and an 18% bitrate saving over the baseline with only a 5–7% encoding time penalty. Additional comparisons of the individual scrambling techniques indicate that Arnold Transform offers a good tradeoff between visual quality and compression ratio. This approach has complete compatibility with standard H.264 decoding, thereby offering a seamless and effective enhancement of the compression process.