When evidence is open to interpretation, opposing counsel can control the narrative and shift the case into a battle of expert testimony. Rather than placing your client’s fate in someone else’s hands, consider improving your audio-video exhibits to become unshakable evidence. There are two methods to accomplish this, enhancement and clarification.
Since its inception, audio and video enhancement has been achieved through a series of independent modifications. Noise is averaged into usable sections, effectively destroying data to aid in perceived clarity, and resolution is increased through interpolation, effectively inventing evidence. Enhancement can also introduce unwanted items (amplified sounds, thickened object trace lines, unrealistic colors, etc…) and is similar to an artist using a paint brush and special lighting to retouch a picture.
Video enhancement remains commonplace because it has a history and only requires minimal computing power. Since enhancement is highly judgment-based, the results and likelihood of a court challenge depend upon the expert’s experience, tools and patience when working on your case.
In early 2012, neighborhood watchman George Zimmerman killed Treyvon Martin, a black teenager after a presumed altercation. Although a local news event, it ignited worldwide racial tensions. The only evidence of Zimmerman’s claimed injuries was a grainy video from Florida’s Sanford Police Department.
Several sources attempted to enhance the presence or absence of wounds upon Zimmerman’s head. Although working from the same video, each enhancement depicted a different version of the truth. ABC was the only agency that reached out to a clarification lab. The clarified results were featured on Good Morning America and ended the speculation. You can see those clarified images at ForensicProtection.com and through a Google image search.
Clarification uses sophisticated math algorithms to automatically rebuild details lost during the original capture and record process. This is analogous to an engineer solving a Sudoku puzzle or putting on prescription eye glasses. More specifically, clarification pairs any two: time, frequency and intensity, and then uses each pair as a training set to repair the third. Every step is co-dependent and may require hours of computer time to restore a single minute of video.
Clarification suppresses noise and improves clarity without inventing or destroying data. Since clarification is a highly automated process, the results are extremely accurate and consistent to a single truth. In certain circumstances, clarification can be further refined through traditional enhancement, a hybrid process known as “E-clarification”.
Clarification reduces the need for experts to defend their work during trial. Unfortunately, switching from being an enhancement artist to a clarification engineer can be a difficult transition, with many experts thriving off the testimonial income that comes from the vagueness of enhancement. For all these reasons, few audio-video experts are skilled in clarification, with most of the technological advances originating at international universities.
Video clarification can be applied to every surveillance recording. It can recover a blurry license plate and brighten the darkest nighttime scene. Clarification can be used to restore VHS tapes, zoom-in on a suspect’s hands, definitively prove if a DUI stop was justified, or support facial identity matching.
Similarly, audio clarification can isolate human speech and suppress distracting sounds. Once clarified, a voice print can be created to identify who said what. Emerging advances in audio software will soon isolate a specific voice from a crowd using voice pattern matching and spatial directionality.
Working with an enhancement or clarification company should be easy and friendly. To accurately determine the level of possible improvements, the vendor must fully clarify a very brief portion of your recording. They will then share that clarified portion so you can make an informed purchase decision. Because modern technology is highly automated, you should never be expected to pay an evaluation fee.
Once you have selected the lab that will provide the clearest results, know that pricing has more to do with the expert’s lifestyle versus their capabilities. A well-staffed lab will deliver your evaluation file within a day or two and should charge about $500 for a full clarification.
Forensic labs also have the ability to determine and document the presence of file tampering. In my next contribution to this blog, I will explain how this is done and how to protect your discovery files from tampering.
By Douglas Carner
Douglas Carner CPP/CHS, is the founder and lead technologist of Forensic Protection, a worldwide clarification and enhancement lab. He can be reached at 818-375-1700 and through his lab
If the goal is restoration, then Clarification is the only true solution. However, Clarification is math intensive (about an hour of quad-core computing per five minutes of video to recover). All of the advances seem to be happening in MatLab and AviSynth. Neither is really user friendly. Recently, Bosch (a huge German company) has become a significant supporter of its development.
Clarification is 100% automatic, restores the loses from compression (it is just math), and always leads back to what the original cameras actually saw. The center-piece of Clarification
is recursive temporal-space-frequency data reconstruction is still in its infancy and reading/testing materials
are fractured like a jig-saw puzzle all over the internet (some of which is proprietary).
The goal is to use each pair to repair the third, and then only use the
repaired data values in determining more accurate settings before
restarting the prior steps.
This recursive loop self-calibrates to a single user defined accuracy
parameter, a balance between accuracy and speed. I have been
experimenting with recursive restoration (aka Clarification) using
dozens of filters, including those of my own creation. My end goal is to license a solution as an automated Video Cleaner built into major
PROBLEM: Surveillance cameras are rarely in focus at the time of the target event.
Enhancement is manual calibration (aka judgment-based) to apply Gaussian focus (e.g. an unsharpen filter) which relies upon a focal singularity with a long tail.
Clarification is applying circular focus to every pixel of every frame (it is, after all, the reverse of the cause) using automatic cross focus point calibration (similar to a cell phone camera).
PROBLEM: Inadequate illumination of a nighttime scene in rec.601 YV12 color space (common surveillance format that stores only 220 shades of brightness and only 33% of RGB color data).
Enhancement is performed with manual (judgment-based) changes to brightness (static addition or subtraction to illumination values) and contrast
(frequency amplification between adjacent pixels), which can destroy illumination data at the max-min levels, and cause illumination blow out.
Clarification is applying automatic white balance and back lighting through histograms and curves (calibrated through maximum contrast deltas) plus
optional automated color/contrast
For peer reviewed work, join and read the extensive library of the IEEE (Institute of Electrical and Electronics Engineers). They sponsor the International Conference on Image Processing which explores the newest clarification technologies. Their annual conference will be in
Paris. Last year it was in Orlando, FL.
I started my journey with articles like this. The latest article I read was on raindrop
Some seemingly unrelated elements exist in the open-source community. Alone they are handy, but when incorporated as part of an adaptive process, they become quite powerful. Two that come to mind are Francois Visagie's
adaptive lens blur repair
and Vit's motion-compensated deinterlacer.
TO BE CONTINUED.....