Spike cameras represent a novel class of neuromorphic imaging devices that capture visual scenes as binary spike streams through temporal integration of light intensity. While offering exceptional dynamic range and energy efficiency, they generate massive binary data volumes that require efficient compression. Existing codecs fail to exploit the unique structure of spike data and lack support for arbitrary temporal resolution. We propose LSE-Codec, a neural lossless compression framework specifically designed for spike cameras that operates independently of frame-rate, as shown in Fig. 1. Our approach introduces Local Spike Embedding (LSE) to reorganize sparse binary spike patterns into compact 8 -bit symbols while preserving spatial structure, followed by a hierarchical autoencoder with autoregressive entropy modeling to predict spike distributions. Evaluated on different datasets under aligned test conditions, our method achieves state-of-the-art performance, reducing bit rates by over 11% compared to JPEG-XL (best-effort mode). This work bridges a critical gap in spike vision systems with frame-rate agnostic lossless coding, enabling efficient storage and transmission of spike data without sacrificing fidelity.

Spike cameras have shown great potential in capturing ultra-high-speed motion scenes by mimicking the retinal fovea's function, especially addressing the challenges of full-time imaging and high dynamic range in an energy-efficient fashion. Leveraging spike emission mechanisms, these cameras achieve extraordinary temporal resolutions in terms of thousands of frames per second, far surpassing traditional imaging devices. However, the resulting data, characterized by its large scale and sufficient temporal imaging nature, poses significant challenges for storage and transmission. In this paper, we propose an advanced lossless compression model for spike data via constructing a novel spike data representation scheme. We first introduce an efficient short-term aggregation method for spike sequences, paired with an intensity remapping technique to mitigate the effects of noise inherent in the spike sampling approach. In addition, we design and propose the Categorical Logit-based Entropy Model (CLEM) by quantitatively and precisely measuring the required code length of the underlying representation to generate an implicit representation that models the unique statistical distribution of spike data. We leverage these findings to introduce a novel learned lossless spike compression model that significantly reduces the data rate while preserving full data fidelity. Extensive experimental results on PKU-Spike-Recon and more real-world spike datasets demonstrate that our approach achieves state-of-the-art (SOTA) performance, with competitive computational complexity. The proposed method illuminates a new path towards lossless compression without encoding the prediction residual for spike data coding.

As a novel bio-inspired imaging device, the spike camera shows remarkable potential in capturing ultra-high-speed motion scenes by simulating the mechanism of the retinal fovea. It achieves a temporal resolution of tens of thousands of Hz through spike emission. However, this capability presents significant challenges in terms of large-scale data storage and transmission, along with stringent fidelity requirements for spike data, thus posing a formidable obstacle to the lossless compression of continuous spike streams. In this paper, we introduce an effective image representation method for spikes, along with an intensity remapping technique to mitigate noise effects in spike streams. Building on this, we propose a learned lossless spike data compression model. To our knowledge, it is the first learning-based model for lossless spike stream compression. Experimental results demonstrate that our method can realize state-of-the-art performance for spike data lossless compression.

The rapid advancement of artificial intelligence (AI) technology has led to the prioritization of standardizing the processing, coding, and transmission of video using neural networks. To address this priority area, the Moving Picture, Audio, and Data Coding by Artificial Intelligence (MPAI) group is developing a suite of standards called MPAI-EEV for "end-to-end optimized neural video coding." The aim of this AI-based video standard project is to compress the number of bits required to represent high-fidelity video data by utilizing data-trained neural coding technologies. This approach is not constrained by how data coding has traditionally been applied in the context of a hybrid framework. This paper presents an overview of recent and ongoing standardization efforts in this area and highlights the key technologies and design philosophy of EEV. It also provides a comparison and report on some primary efforts such as the coding efficiency of the reference model. Additionally, it discusses emerging activities such as learned Unmanned-Aerial-Vehicles (UAVs) video coding which are currently planned, under development, or in the exploration phase. With a focus on UAV video signals, this paper addresses the current status of these preliminary efforts. It also indicates development timelines, summarizes the main technical details, and provides pointers to further points of reference. The exploration experiment shows that the EEV model performs better than the state-of-the-art video coding standard H.266/VVC in terms of perceptual evaluation metric.