Bienvenido! - Willkommen! - Welcome!

Bitácora Técnica de Tux&Cía., Santa Cruz de la Sierra, BO
Bitácora Central: Tux&Cía.
Bitácora de Información Avanzada: Tux&Cía.-Información
May the source be with you!

Monday, June 11, 2012

Enhancing video quality video-quality-enhancement
If the video is encoded at a low resolution such as 320x240, it will never match DVD quality. likewise, a high resolution encoded at a low bit rate (720x480 1.1mbps) will never match DVD quality (which is typically 4-6mbps).
The reason for this is that virtually all video codecs are considered lossy. Meaning they delete data as part of the compression mechanism. So there is no way to recover this without the source from which these files were made.
And if you have an upconverting DVD player, it's only going to make these limitations more apparent if you try and scale the image up to higher resolutions.
Anyway, if you take nothing else away from this, it should be that you will never achieve quality better than that of your source material. At least not without a whole host of very expensive professional video editing tools and a lot of time and patience. Some DVD players do offer their own set of post processing features, but they can really only do so much, and unless it is now, or once was, a fairly high end DVD player, using post processing features might well cause the AV sync to start drifting. Meaning you see someone's lips moving before or after you hear the audio component.

For stantdard video cameras
you need the following hardware with a good TIME BASE CORRECTOR
Topaz Moment 3.1

Video Enhancer 1.9
vReveal - Free Video Enhancement Software

IP Video Surveillance Live! DEMO 1.0
IP Video Surveillance Live! RTSP DEMO 1.0
Video super-resolution coming to consumer software
Video super-resolution is a technique to increase the resolution of a movie by exploiting the redundancy between frames. It’s easiest to understand the technique by first thinking of the corresponding technology in images. It’s possible to increase the effective resolution of an image by taking multiple pictures, each offset by a fraction of a pixel, and subsequently joining them. Some early digital photo cameras had this pixel-shifting technology builtin; the CCD was physically displaced (or maybe it was a lens) by half a pixel horizontally and two images would be stitched together. I remember we had an early JVC camera from the late 90′s back when I was working in an electronics store that cheerfully advertised being able to increase its resolution in this manner to a then-whopping 3 megapixels.
Pixel-shifting will not truly double the resolution of the image by itself — pixels still integrate over a large area of space, so the images must be deconvolved against the point spread function of a CCD pixel to recover the true image. Blind deconvolution is of course prone to noise, but advances in image modelling have made natural image deconvolution quite reliable. As in the case of denoising, the more sophisticated your prior over natural images is (smooth, sparse in a wavelet basis, Gaussian scale mixture, etc.), the better the results of the deconvolution.
Video super-resolution uses the same basic premise. Subsequent frames in a video are only slightly different from each other — this is what makes video highly compressible. You can model the change from one frame to the next as a non-rigid transformation. Once optic flow has been estimated images can be aligned by undoing the warping created by optic flow. Then subsequent video frames can be stitched together as in image pixel-shifting to form a super-resolution video. Actual algorithms for video super-resolution can blur the lines between the optic flow, stitching and deconvolution steps; see this IEEE article for more details on the subject.
These technologies have been around for a while to be used by researchers. For example, this Matlab package implements several different video super-res techniques, while this one implements one specific technique in detail. I’m sure you can think of many applications to scientific image processing.

Video super-resolution is slowly making its to consumer products. MotionDSP offers the Ikena product aimed at forensic professionals to perform super-resolution on the fly. It can be used to read a license plate number (above) or capture the image of a criminal, for example. Take a look at this video to see this in action; it looks straight out of a cop show.
Now they’ve taken this technology and offered it as part of a consumer-oriented program called vReveal. The company indicates that it either removed the feature, renamed it, or integrated it into other components of the software going from version 2 to version 3. I’m sure you can find the old version on the interwebs, however. The same technology is used in recent iterations of the software to perform image stabilization and denoising. Here’s a demo from another company of of another super video-resolution product.
Related technology for noise reduction is also available in plugins to video editing software. Here’s the result of applying the technology to noisy video shot at night. Pretty dramatic.
Unfortunately it’s difficult to relate what these commercial, closed-source packages to specific algorithms in the literature; although a lot of companies claim to increase video resolution or reduce noise, it’s hard to say what it is exactly the products do behind the scenes. Nevertheless, it’s interesting to see industry follow up on fundamental research, even if it is only to enhance silly home videos. I’m sure better software will become available in this area as soon as somebody has figured out how to make money out of this thing.
Basics: Increasing image resolution

One of the most commonly asked questions in relation to graphics software is how to increase the size of an image without getting blurring and jagged edges. New users are often surprised when they resize an image and find that the quality is severely degraded. Experienced users are all too familiar with the problem. The reason for the degradation is because bitmapped, or raster, image types are limited by their pixel resolution. When you attempt to resize these types of images, your software either has to increase the size of each individual pixel - resulting in a jagged image - or it has to "guess" at the best way to add pixels to the image to make it larger.
Not long ago, there weren't many options for increasing resolution other than using your editing software's built-in resampling methods. Today, we are faced with more possibilities than ever. Of course, it's always best to capture the resolution you need right from the beginning. If you have the option to rescan an image at a higher resolution, by all means, you should do that before resorting to software solutions. And if you have the money to put into a camera capable of higher resolutions, you might find that money is better spent than if you were to put it into a software solution. Having said that, there are often times when you may have no other choice than to resort to software. When that time comes, here's the information you should know.
Resizing vs. Resampling
Most software only has one command for both resizing and resampling. Resizing an image involves changing the print dimensions without changing the total pixel dimensions. As the resolution is increased, the print size becomes smaller, and vice versa. When you increase resolution without changing pixel dimensions, there is no loss in quality, but you must sacrifice print size. Resizing an image using resampling, however, involves changing the pixel dimensions and will always introduce a loss in quality. That's because resampling uses a process called interpolation for increasing the size of an image. The interpolation process estimates the values of the pixels the software needs to create based on the existing pixels in the image. Resampling via interpolation results in serious blurring of the resized image, especially in areas where there are sharp lines and distinct changes in color.
About Image Size & Resolution
Common Interpolation Methods
Photo editing software generally offers a few different interpolation methods for calculating new pixels when an image us upsampled. Here are descriptions of the three methods available in Photoshop. If you don't use Photoshop, your software probably offers similar options although they may use slightly different terminology.
  • Bicubic is the slowest but produces the best estimation of new pixel values.
  • Bilinear is faster than bicubic, but does a poorer job. Both bicubic and bilinear interpolation result in a blurred image, especially when upsampling.
  • Nearest Neighbor doesn't use interpolation. It simply takes the value of the neighboring pixels and adds new pixels without averaging them. This is when you get the jaggies or stair-step effect.
Note that there are more than just these three methods of interpolation and even using the same method in different software may produce different results. In my experience, I have found that Photoshop offers the best bicubic interpolation of any other software that I have compared.
Photoshop's Image Size Dialog Box

No comments: