Overview
Advancements in video compression are critical to meeting the growing demand for high-quality video streaming, particularly with the rise of 4K, 8K, and even 16K resolutions. Traditional codecs like HEVC (H.265) and VVC (H.266) have achieved significant improvements in compression efficiency through hand-crafted tools. However, the integration of neural networks into video coding has emerged as a transformative approach, offering new possibilities for higher compression efficiency and reduced computational complexity.
Challenges in Traditional Video Compression
Traditional video codecs like HEVC and VVC rely on sophisticated frameworks, including quadtree-based partitioning and rate-distortion optimization (RDO), to achieve high compression efficiency. For example:HEVC/H.265 introduced quadtree-based coding units (CUs) with 35 intra-prediction modes. VVC/H.266 expanded this with multi-type tree (MTT) partitioning schemes (binary and ternary trees) and 67 intra-prediction modes for finer granularity. While these methods achieve excellent compression rates, they come at the cost of high computational complexity, particularly in encoding processes like motion estimation and intra-mode decision-making. This has spurred research into neural network-based solutions to address these challenges.
Video Compression Frameworks
Versatile Learned Video Compression (VLVC): This framework uses 3D motion vector fields (voxel flows) for spatial-temporal motion compensation. It decouples inter-prediction modes from the codec framework design, enabling more flexible compression strategies. DeepCoder: A convolutional neural network (CNN)-based framework that replaces traditional motion estimation with learned models, improving rate-distortion performance while reducing complexity. These methods demonstrate state-of-the-art performance in terms of Peak Signal-to-Noise Ratio (PSNR) while maintaining lower bitrates.
Conclusion
Neural network-based video compression represents a paradigm shift that complements traditional methods while addressing their limitations. As research progresses, these techniques are poised to redefine the landscape of video streaming and storage efficiency in an era dominated by ultra-high-definition content and immersive media experiences like VR/AR.
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