Video Compression With Rate-Distortion Autoencoders (1908.05717v2)

Abstract: In this paper we present a a deep generative model for lossy video compression. We employ a model that consists of a 3D autoencoder with a discrete latent space and an autoregressive prior used for entropy coding. Both autoencoder and prior are trained jointly to minimize a rate-distortion loss, which is closely related to the ELBO used in variational autoencoders. Despite its simplicity, we find that our method outperforms the state-of-the-art learned video compression networks based on motion compensation or interpolation. We systematically evaluate various design choices, such as the use of frame-based or spatio-temporal autoencoders, and the type of autoregressive prior. In addition, we present three extensions of the basic method that demonstrate the benefits over classical approaches to compression. First, we introduce semantic compression, where the model is trained to allocate more bits to objects of interest. Second, we study adaptive compression, where the model is adapted to a domain with limited variability, e.g., videos taken from an autonomous car, to achieve superior compression on that domain. Finally, we introduce multimodal compression, where we demonstrate the effectiveness of our model in joint compression of multiple modalities captured by non-standard imaging sensors, such as quad cameras. We believe that this opens up novel video compression applications, which have not been feasible with classical codecs.

Video Compression With Rate-Distortion Autoencoders: An Overview

The paper "Video Compression With Rate-Distortion Autoencoders" by Habibian et al. explores a novel approach to lossy video compression using deep generative models. The authors propose a rate-distortion autoencoder framework that integrates a 3D autoencoder with an autoregressive prior to improve video compression performance.

Key Contributions

1. Rate-Distortion Autoencoder Design: The authors introduce a model that utilizes a 3D autoencoder with discrete latent spaces to facilitate efficient entropy coding, significantly outperforming prior learned video compression networks based on techniques like motion compensation or interpolation. The model optimizes a rate-distortion loss akin to the ELBO from variational autoencoders (VAE), providing a robust mechanism for handling lossy compression while maintaining video quality.
2. Extensions and Novel Applications:
   • Semantic Compression: By emphasizing significant objects during encoding, the model allocates more bits to crucial areas of the video, thus enhancing the reconstruction of objects of interest such as people.
   • Adaptive Compression: Demonstrating adaptability, the model can be fine-tuned to specific domains such as those with limited variability, for example, autonomous car videos, optimizing compression performance in specialized contexts.
   • Multimodal Compression: The paper explores joint compression using multiple modalities, such as data from quad cameras, highlighting potential new applications beyond standard video compression tasks.
3. Rate-Distortion Loss Framework: The research further elucidates the connection between rate-distortion autoencoders and VAEs, emphasizing a deterministic encoder strategy that avoids stochastic encoding variations. This choice circumvents unnecessary bitrate increases without enhancing video reconstruction quality.

Experimental Evaluation

The authors perform extensive evaluations, contrasting various architectural choices like frame-based versus spatio-temporal autoencoders and diverse autoregressive priors. Results show that their approach not only outpaces existing state-of-the-art learned compression methods but also challenges traditional codecs like HEVC/H.265 and AVC/H.264 under certain conditions, particularly in restricted settings where inter-frame compression is limited.

Implications and Future Directions

From a theoretical perspective, the paper's contributions offer a solid groundwork for further explorations in generative video compression. The deterministic approach to encoding within the VAE framework potentially paves the way for more efficient models in future research.

Practically, the demonstrated semantic, adaptive, and multimodal compression capabilities could transform video codec applications, making them more responsive and tailored to specific needs, as well as scalable across differing modalities of sensor data.

The prospects for further advancements in AI-driven video compression are promising, particularly in areas that require real-time adaptive qualities or need to integrate multisensor data comprehensively.

In conclusion, the paper provides a detailed exposition of a practical, theoretically sound method for video compression, highlighting both the immediate benefits and future potential of leveraging deep generative models in this domain.