#neuromorphic #decoding #event #asynchronous #video

bin+lib adder-codec-rs

Encoder/transcoder/decoder for ADΔER (Address, Decimation, Δt Event Representation) streams. Currently, only implemented for raw (uncompressed) streams. Includes a transcoder for casting framed video into an ADΔER representation in a manner which preserves the temporal synchronicity of the source, but enables many-frame intensity averaging on a per-pixel basis, and high dynamic range.

11 releases

Uses new Rust 2021

0.1.13 Sep 1, 2022
0.1.12 Aug 31, 2022
0.1.0 Jul 21, 2022

#19 in Multimedia

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ADDER-codec-rs

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Encoder/transcoder/decoder for ADΔER (Address, Decimation, Δt Event Representation) video streams. Currently, only implemented for raw (uncompressed) streams. Includes a transcoder for casting framed video into an ADΔER representation in a manner which preserves the temporal synchronicity of the source, but enables many-frame intensity averaging on a per-pixel basis and extremely high dynamic range.

Source 8-bit image frame with shadows boosted (source video) Frame reconstructed from ADΔER events, generated from 48 input frames, with shadows boosted. Note the greater dynamic range and temporal denoising in the shadows.

Background

The ADΔER (pronounced "adder") representation is inspired by the ASINT camera design by Singh et al. It aims to help us move away from thinking about video in terms of fixed sample rates and frames, and to provide a one-size-fits-all ("narrow waist") method for representing intensity information asynchronously.

Under the ASINT model, a pixel $\langle x,y\rangle$ continuously integrates light, firing an ADΔER event $\langle x,y,D,\Delta t\rangle$ when it accumulates $2^D$ intensity units (e.g., photons), where $D$ is a decimation threshold and $\Delta t$ is the time elapsed since the pixel last fired an event. we measure $t$ in clock “ticks,'' where the granularity of a clock tick length is user-adjustable. In a raw ADΔER stream, the events are time-ordered and spatially interleaved. An ADΔER event directly specifies an intensity, $I$, by $I \approx \frac{2^D}{\Delta t}$. The key insight of the ASINT model is the dynamic, pixelwise control of $D$. Lowering $D$ for a pixel will increase its event rate, while raising $D$ will decrease its event rate. With this multi-faceted $D$ control, we can ensure that pixel sensitivities are well-tuned to scene dynamics.

Practically speaking, it's most useful to think about ADΔER in reference to the source data type. In the current iteration of this package, I only provide tools for transcoding framed video to ADΔER, but in the future I will release tools for transcoding data from real-world event cameras (e.g., DVS and DAVIS).

In the context of framed video, ADΔER allows us to have multi-frame intensity averaging for stable (unchanging) regions of a scene. This can function both to denoise the video and enable higher dynamic range, all while preserving the temporal synchronicity of the source. See the info on simultaneous transcoding to quickly test this out!

Setup

If you just want to use the hooks for encoding/decoding ADΔER streams (i.e., not a transcoder for producing the ADΔER events for a given source), then you can include the library by adding the following to your Cargo.toml file:

adder-codec-rs = {version = "0.1.13", features = ["raw-codec"]}

If you want to use the provided transcoder(s), then you have to install OpenCV 4.0+ according to the configuration guidelines for opencv-rust. Then, include the library in your project as normal:

adder-codec-rs = "0.1.13"

Examples

Clone this repository to run examples provided in /examples and /src/bin

Transcode framed video to ADΔER events

We can transcode an arbitrary framed video to the ADΔER format. Run the program /examples/framed_video_to_adder.rs. You will need to adjust the parameters for the FramedSourceBuilder to suit your needs, as described below.

 let mut source =
        // The file path to the video you want to transcode, and the bit depth of the video
        FramedSourceBuilder::new("~/Downloads/excerpt.mp4".to_string(),
                                 SourceCamera::FramedU8)    
        
        // Which frame of the input video to begin your transcode
        .frame_start(1420)  
        
        // Input video is scaled by this amount before transcoding
        .scale(0.5)         
        
        // Must be true for us to do anything with the events
        .communicate_events(true)   
        
        // The file path to store the ADΔER events
        .output_events_filename("~/Downloads/events.adder".to_string())     
        
        // Use color, or convert input video to grayscale first?
        .color(false)       
        
        // Positive and negative contrast thresholds. Larger values = more temporal loss. 0 = nearly no distortion.
        .contrast_thresholds(10, 10)    
        
        // Show a live view of the input frames as they're being transcoded?
        .show_display(true) 
        
        .time_parameters(5000,  // The reference interval: How many ticks does each input frame span?
                         300000,    // Ticks per second. Must equal (reference interval) * (source frame rate)
                         3000000)   // Δt_max: the maximum Δt value for any generated ADΔER event
        .finish();

Generate framed video from ADΔER events

We can also transform our ADΔER file back into a framed video, so we can easily view the effects of our transcode parameters. Run the program /examples/events_to_instantaneous_frames.rs. You will need to set the input_path to point to an ADΔER file, and the output_path to where you want the resulting framed video to be. This output file is in a raw pixel format for encoding with FFmpeg: either gray or bgr24 (if in color), assuming that we have constructed a FrameSequence<u8>. Other formats can be encoded, e.g. with FrameSequence<u16>, FrameSequence<f64>, etc.

To take our raw frame data and encode it in a standard format, we can use an FFmpeg command as follows:

ffmpeg -f rawvideo -pix_fmt gray -s:v 960x540 -r 60 -i ./events.adder -crf 0 -c:v libx264 -y ./events_to_framed.mp4

Simultaneously transcode framed video to ADΔER events and back to framed video

This is the most thorough example, complete with an argument parser so you don't have to edit the code. Run the program /src/bin/adder_simulproc.rs, like this:

cargo run --release --bin transcode_and_frame_simultaneous -- 
    --scale 1.0 
    --input-filename "/path/to/video"
    --output-raw-video-filename "/path/to/output_video"
    --c-thresh-pos 10
    --c-thresh-neg 10

The program will re-frame the ADΔER events as they are being generated, without having to write them out to a file. This lets you quickly experiment with different values for c_thresh_pos, c_thresh_neg, ref_time, delta_t_max, and tps, to see what effect they have on the output.

Inspect an ADΔER file

Want to quickly view the metadata for an ADΔER file? Just execute:

cargo run --release --bin adderinfo -- -i /path/to/file.adder -d

Alternatively, you can install this program for the current user with cargo install adder-codec-rs --bin adderinfo, then run with adderinfo -- -i /path/to/file.adder -d. This program is analagous to ffprobe for framed video.

The -d flag enables the calculation of dynamic the ADΔER file's dynamic range. This can take a while, since each event must be decoded to find the event with the maximum intensity and the minimum intensity. Example output:

Dimensions
	Width: 960
	Height: 540
	Color channels: 3
Source camera: FramedU8 - Framed video with 8-bit pixel depth, unsigned integer
ADΔER transcoder parameters
	Codec version: 1
	Ticks per second: 120000
	Reference ticks per source interval: 5000
	Δt_max: 240000
File metadata
	File size: 1114272056
	Header size: 29
	ADΔER event count: 111427201
	Events per pixel: 214
Dynamic range
	Theoretical range:
		114 dB (power)
		37 bits
	Realized range:
		27 dB (power)
		9 bits

Direct usage

Encode a raw stream:

let mut stream: RawStream = Codec::new();
match stream.open_writer("/path/to/file") {
    Ok(_) => {}
    Err(e) => {panic!("{}", e)}
};
stream.encode_header(500, 200, 50000, 5000, 50000, 1);

let event: Event = Event {
        coord: Coord {
            x: 10,
            y: 30,
            c: None
        },
        d: 5,
        delta_t: 1000
    };
let events = vec![event, event, event]; // Encode three identical events, for brevity's sake
stream.encode_events(&events);
stream.close_writer();

Read a raw stream:

let mut stream: RawStream = Codec::new();
stream.open_reader(args.input_filename.as_str())?;
stream.decode_header();
match self.stream.decode_event() {
    Ok(event) => {
        // Do something with the event
    }
    Err(_) => panic!("Couldn't read event :("),
};
stream.close_reader();

Cite this work

If you write a paper which references this software, we ask that you reference the following papers on which it is based. Citations are given in the BibTeX format.

Motion segmentation and tracking for integrating event cameras

@inproceedings{10.1145/3458305.3463373,
author = {Freeman, Andrew C. and Burgess, Chris and Mayer-Patel, Ketan},
title = {Motion Segmentation and Tracking for Integrating Event Cameras},
year = {2021},
isbn = {9781450384346},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3458305.3463373},
doi = {10.1145/3458305.3463373},
abstract = {Integrating event cameras are asynchronous sensors wherein incident light values may be measured directly through continuous integration, with individual pixels' light sensitivity being adjustable in real time, allowing for extremely high frame rate and high dynamic range video capture. This paper builds on lessons learned with previous attempts to compress event data and presents a new scheme for event compression that has many analogues to traditional framed video compression techniques. We show how traditional video can be transcoded to an event-based representation, and describe the direct encoding of motion data in our event-based representation. Finally, we present experimental results proving how our simple scheme already approaches the state-of-the-art compression performance for slow-motion object tracking. This system introduces an application "in the loop" framework, where the application dynamically informs the camera how sensitive each pixel should be, based on the efficacy of the most recent data received.},
booktitle = {Proceedings of the 12th ACM Multimedia Systems Conference},
pages = {111},
numpages = {11},
keywords = {HDR, spike compression, image reconstruction, simulation, event cameras, object tracking, entropy encoding, motion segmentation, asynchronous systems},
location = {Istanbul, Turkey},
series = {MMSys '21}
}

Integrating Event Camera Sensor Emulator

@inproceedings{10.1145/3394171.3414394,
author = {Freeman, Andrew C. and Mayer-Patel, Ketan},
title = {Integrating Event Camera Sensor Emulator},
year = {2020},
isbn = {9781450379885},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394171.3414394},
doi = {10.1145/3394171.3414394},
abstract = {Event cameras are biologically-inspired sensors that upend the framed, synchronous nature of traditional cameras. Singh et al. proposed a novel sensor design wherein incident light values may be measured directly through continuous integration, with individual pixels' light sensitivity being adjustable in real time, allowing for extremely high frame rate and high dynamic range video capture. Arguing the potential usefulness of this sensor, this paper introduces a system for simulating the sensor's event outputs and pixel firing rate control from 3D-rendered input images.},
booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
pages = {45034505},
numpages = {3},
keywords = {asynchronous systems, image reconstruction, spike compression, event cameras, HDR, simulation},
location = {Seattle, WA, USA},
series = {MM '20}
}

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