Report on Current Developments in Neural Video Representation and Compression
General Direction of the Field
The field of neural video representation and compression is witnessing significant advancements, particularly in the areas of redundancy reduction, consistency preservation, and efficiency in encoding and decoding processes. Recent research is focusing on leveraging implicit neural representations (INRs) to embed video signals into compact neural networks, thereby achieving higher compression rates and better preservation of video quality. The emphasis is on developing methods that not only reduce the redundancy in video features but also enhance the network's ability to learn relationships between frames, leading to more efficient and effective video compression.
One of the key trends is the integration of high-frequency components and feature differences between adjacent frames into the neural representation process. This approach aims to minimize redundancy while ensuring that the network can smoothly learn temporal dependencies, which is crucial for maintaining video consistency. Additionally, there is a growing interest in accelerating the encoding and decoding processes within INRs, with innovations like transformer-based hyper-networks and parallel decoders showing promising results in significantly reducing processing times.
Another notable development is the exploration of variable rate coding in learned image compression, where parametric quantization methods are being introduced to enable flexible bitrate adjustments without the need for multiple encoder-decoder pairs. This approach not only simplifies deployment but also reduces training time and storage costs.
Furthermore, the field is seeing advancements in enhancing the dynamic feature retention capabilities of convolutional neural networks (CNNs) through novel architectural units like the "Squeeze-and-Remember" block. These units aim to mimic human-like memory functionalities, improving the network's ability to use learned information in new contexts and enhancing performance in image processing tasks.
Noteworthy Papers
Fast Encoding and Decoding for Implicit Video Representation: This paper introduces a transformer-based hyper-network for fast encoding and a parallel decoder for efficient video loading, achieving significant speed-ups in both processes.
STanH: Parametric Quantization for Variable Rate Learned Image Compression: The proposed STanH quantizer enables variable rate coding with comparable efficiency to state-of-the-art methods, significantly reducing deployment complexity and storage costs.
Unleashing Parameter Potential of Neural Representation for Efficient Video Compression: This work explores parameter reuse in INR video compression, significantly enhancing rate-distortion performance through a novel parameter reuse scheme.
