---
title: 'ADΔER: Lightweight Asynchronous Event Codec'
url: https://www.emergentmind.com/topics/ad-er-address-decimation-t-event-representation
type: topic
---

# ADΔER: Lightweight Asynchronous Event Codec

ADΔER (Address, Decimation, Δt Event Representation) is a lightweight, asynchronous pixel-stream representation designed to unify traditional framed video and modern event-camera modalities under a single codec and API. It provides an intermediate video representation and system framework supporting source-modeled lossy compression, efficient processing, and backward compatibility with frame-based applications while exposing fine-grained control over event-rate and algorithmic workload [2408.06248].

## 1. Formal Definition and Data Representation

An ADΔER event is formally defined as a five-tuple:

$$
e = \langle x, y, c, D, t \rangle
$$

where:
- $x, y \in \mathbb{Z}_{16}$: 16-bit pixel coordinates (row, column)
- $c \in \mathbb{Z}_8$: 8-bit color channel index (0 = Y/luma, or 0–2 = RGB)
- $D \in \mathbb{Z}_8$: 8-bit decimation exponent
- $t \in \mathbb{Z}_{32-64}$: absolute timestamp in integer clock "ticks"

Each event encodes its own spatial address—removing the need for run-length encoding or explicit full-frame scanouts commonly required in frame-based representations. The asynchrony and sparsity of ADΔER permit highly efficient storage and computation, as events are only generated when pixel-level intensity changes cross configured thresholds.

Temporal dynamics use per-pixel temporal binning:
$$
\Delta t(e) = t(e) - t(e^{-})
$$
with $t(e^{-})$ as the timestamp for the previous event at the same $(x, y, c)$. $\Delta t_{max}$ (a user-settable latency span) bounds the temporal granularity; smaller values capture more detail at the cost of more events, while larger values throttle the event-rate on static pixels.

Intensity is estimated during reconstruction by:
$$
I(e) \propto 2^D / \Delta t(e)
$$
where only $D$ and $t$ are stored, and $\Delta t$ is recomputed during playback. This use of absolute timestamps (rather than chained $\Delta t$) ensures that errors in lossy compression affect only immediate intensity recovery.

The “decimation” exponent $D$ sets the per-pixel intensity integration threshold. Each pixel maintains an accumulator $A(t)$. When $A(t) \geq 2^{D(t)}$, the pixel emits an event, subtracts $2^{D(t)}$ from $A(t)$, and may increment $D(t)$ to coarsen its rate (throttle). A global or adaptive contrast threshold $M$ enforces emission if the signal departs too far from the last value, enabling rate–distortion tradeoff and prioritization of bandwidth to selected pixels or regions.

## 2. System Architecture and Codec Workflow

The ADΔER system architecture consists of three conceptual layers:

| Layer                | Functionality                                                          | Remark                                           |
|----------------------|-----------------------------------------------------------------------|--------------------------------------------------|
| Acquisition          | Source transcoding from framed, DVS, or DAVIS sensors                 | Modality-agnostic intake                         |
| Representation/Compression | Temporal/spatial cube organization, arithmetic coding, ADU partitioning     | Efficient, source-modeled rate–distortion        |
| Applications         | Frame reconstruction, async callbacks, DVS back-conversion            | Backward compatibility and native event support  |

**Acquisition layer**:
- **Framed→ADΔER**: Video frames are sampled (e.g., via FFmpeg), quantized to reference intervals ($\Delta t_{ref}$), and then processed per-pixel using accumulators that emit events upon reaching $2^D$ integration.
- **DVS→ADΔER**: Events from a DVS camera (polarity events $\langle x,y,p,t \rangle$) are accumulated using inferred absolute log-intensity, converted to integration events, and fed to the same ADΔER accumulator model.
- **DAVIS→ADΔER**: APS frames are deblurred to form latent intensity images (via EDI) and DVS events provide inter-frame motion, both feeding into the ADΔER model for joint representation.

**Representation/compression layer** uses fixed-duration Application Data Units (ADUs) spanning $\Delta t_{adu}$ (e.g., one second, or 30 frames). Events are grouped spatially (e.g., 16×16 cubes), intra-coded for the first event in each cube, and inter-coded via prediction and arithmetic coding (CABAC). This yields an efficient two-stage pipeline—temporal averaging for source modeling, followed by entropy coding—which furnishes a direct rate–distortion tradeoff.

**Application layer** permits multiple downstream workflows:
- **Frame reconstruction**: Per-pixel accumulators update reconstructed frames for legacy applications.
- **Asynchronous algorithms**: Event-driven algorithms (e.g., FAST feature detection) subscribe to per-pixel callbacks, allowing rapid, local processing.
- **DVS-back conversion**: By thresholding on log-intensity, ADΔER events can be re-emitted as DVS polarity events, maintaining compatibility with neuromorphic and pre-existing event-based pipelines.

## 3. Evaluation and Benchmark Results

ADΔER’s performance was evaluated across traditional framed video, DAVIS fusion, object detection/tracking, and feature detection and control. The principal findings include:

- **Framed-video transcode** (YT-UGC subset, 360p–1080p):
  - For contrast threshold $M = 10$, the median event-rate is 46% of the $M=0$ baseline.
  - Video quality (VMAF) drops from approximately 99 to 95; PSNR ≈ 44 dB at high quality.
  - Encoding speed exceeds 250 FPS raw and 100 FPS compressed (on 8-core CPUs).
- **DAVIS fusion** (DVSNOISE20):
  - Fusion modes: (i) EDI-reconstruct→framed→ADΔER, (ii) deblur+DVS→ADΔER, (iii) DVS only→ADΔER.
  - Event recovery: Modes (ii, iii) recover >80% of original DVS events; mode (i) <10%.
  - Latency: Mode (iii) achieves ≈0.4 s per packet, mode (ii) ≈2.1 s, mode (i) >20 s at high FPS.
- **Object Detection & Tracking**:
  - YOLOv5 on BDD100K: mAP +1.2% overall (+3.9% in “rider” class) using (intensity, D, Δt) as RGB channels.
  - MIL-tracker (NFS data): ≤1 event/pixel/frame; AUC drop 0.9% compared to raw frames.
- **Feature Detection & Rate Control** (VIRAT):
  - Asynchronous FAST (on ADΔER) achieves median speedup of 43.7% over OpenCV FAST at "Low" CRF (≈0.5 event/pixel/frame).
  - Source-modeled compression (CRF {0, 3, 6, 9}) yields 2.5:1 reduction over raw ADΔER with 1–1.6 dB reduction in PSNR.
  - Region-of-interest decimation provides +3–6 dB PSNR around moving objects without additional CPU cost.
- **DVS→ADΔER→DVS object detection** (GEN1 automotive, HMNet):
  - Lossless→Low CRF reduces DVS bitrate by 50–85%; mAP drop for large objects: 0.2% (ADΔER+CRF) vs 8.4% (random drop) for 76.8% bitrate reduction.

Scene temporal redundancy strongly governs compression ratio (low motion yields >5:1 compression) and processing throughput (sparser events enable faster asynchronous algorithms).

## 4. Content-Adaptive Rate and Application Control

ADΔER exposes a unique mechanism whereby application speed is directly governed by the event-rate, which itself depends on scene activity and compressive settings. Bandwidth, accuracy, and processing speed become jointly controllable via simple parameters:

- **Global CRF ($\in [0..9]$)** sets $M$, $M_{max}$, $M_v$, and cluster radius. Increasing CRF predictably reduces event-rate and computational cost at the expense of PSNR (≈2.5–5 dB per step).
- **Region-of-interest (ROI) decimation** modulates $D_{app}$ near features/tracked objects, leaving other pixels throttled towards $\Delta t_{max}$.
- These mechanisms permit dynamic, local adaptation of event generation. For example, a surveillance system can allocate higher event rates to PTZ zones upon intrusion detection while throttling bandwidth elsewhere.

A plausible implication is enablement of highly scalable machine vision pipes in edge or cloud environments, where bandwidth and computation are matched to situational requirements.

## 5. Broader Applications and Implications

### 5.1 Large-Scale Surveillance

Conventional framed solutions lead to either high storage or global bitrate cuts, which degrade both human (VMAF) and machine (e.g., detection accuracy) performance. ADΔER’s edge-transcoding enables per-region, per-time, and per-application adaptation, reducing bitrate up to 90% in static regions and enabling sub-second detection without frame buffering. This provides a unified basis for fusing emerging event sensors (e.g., Aeveon) alongside conventional cameras.

### 5.2 Resource-Constrained Sensing

Embedded and mobile devices are limited in their ability to transmit high-resolution or high-frame-rate video. By adapting per-pixel decimation—as fast-moving regions trigger more events and static regions are aggressively throttled—ADΔER supports always-on computer vision even in highly resource-limited (e.g., IoT, drone) settings, removing the need for sensor-specific codecs.

## 6. Conclusion and Future Directions

ADΔER establishes a universal, sparse, and compressible intermediate format for video from a broad spectrum of camera modalities, including frames, DVS, DAVIS, and emerging intensity-event cameras. The representation’s exposure of raw event-rate as both a compression and algorithmic workload metric enables content-adaptive pipelines spanning software and hardware. Open-source implementations are available (https://github.com/ac-freeman/adder-codec-rs), providing a unified, narrow-waist event API to accelerate research and deployment of next-generation vision systems [2408.06248].

Source: https://www.emergentmind.com/topics/ad-er-address-decimation-t-event-representation