FIASCO—An Open-Source Fractal Image and Sequence Codec
A picture is worth a thousand words—a frequently used sentence to introduce the need for digital image processing. And indeed, a wide variety of aspects in our life is influenced by digital images in the meantime. For instance, in the World Wide Web not only still pictures but also small video sequences are used to enhance the design of web pages. However, the usage of digital images has a major drawback. An enormous amount of data has to be transmitted and stored each time an image or video is requested.
For example, a single uncompressed frame of a high definition television (HDTV) screen (resolution of 1280x720 pixels, 24 bits per pixel) requires more than 2MB memory. When assuming a display rate of 60 frames per second (HDTV), one second of a video movie already requires more than 165MB, summing up to a total of 2,000 compact discs for a movie of 120 minutes! Clearly, downloading such an uncompressed video stream is impossible, even though fast Internet connections like asymmetric digital subscriber line (ADSL) are getting more popular now.
So image and video compression systems—like FIASCO, the fractal image and video codec—are mandatory in handling such enormous amount of data.
Different solutions are applicable to compress image data: for instance, the resolution of the frames can be reduced as well as the frame rate. However, this reduction is not sufficient. In general, image sequences typically contain three different types of redundancy that can be exploited (see Resources):
spatial redundancy, which is due to the correlation between neighboring pixels
spectral redundancy, which is due to the correlation between different color bands (red, green and blue components)
temporal redundancy, which is due to the correlation between subsequent video frames
The goal of any image compression system is to recognize and remove these redundancies. The following two compression approaches are widely used:
lossless, or reversible: the decoded image is numerically identical to the original image (the file size is typically reduced by 50%); this is useful if the image is computationally processed any further
the decoded image contains more or less artifacts (file size less than 10% of the original amount of data); this is useful in low bit-rate applications like the World Wide Web
FIASCO—the fractal image and sequence codec—is intended as a replacement for JPEG and MPEG for very low bit rates (see Resources). It provides the following features:
state-of-the-art image and video compression (combined in one application)
real-time software-based decoding
open-source implementation
FIASCO compressed images are typically much smaller than JPEG files (at low bit rates), while the image quality is still acceptable. For example, see Figure 1 where you see images compressed by JPEG and FIASCO (1:220 in Figure 1A and 1B and 1:100 in Figure 1C and 1D compression ratio, i.e., 0.5% and 1% respectively, of the original file size).

Figure 1A. FIASCO 1:220

Figure 1B. JPEG 1:220

Figure 1C. FIASCO 1:100

Figure 1D. JPEG 1:100
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Sponsored by AMD
If you already use virtualized infrastructure, you are well on your way to leveraging the power of the cloud. Virtualization offers the promise of limitless resources, but how do you manage that scalability when your DevOps team doesn’t scale? In today’s hypercompetitive markets, fast results can make a difference between leading the pack vs. obsolescence. Organizations need more benefits from cloud computing than just raw resources. They need agility, flexibility, convenience, ROI, and control.
Stackato private Platform-as-a-Service technology from ActiveState extends your private cloud infrastructure by creating a private PaaS to provide on-demand availability, flexibility, control, and ultimately, faster time-to-market for your enterprise.
Sponsored by ActiveState
| Non-Linux FOSS: libnotify, OS X Style | Jun 18, 2013 |
| Containers—Not Virtual Machines—Are the Future Cloud | Jun 17, 2013 |
| Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer | Jun 12, 2013 |
| Weechat, Irssi's Little Brother | Jun 11, 2013 |
| One Tail Just Isn't Enough | Jun 07, 2013 |
| Introduction to MapReduce with Hadoop on Linux | Jun 05, 2013 |
- Containers—Not Virtual Machines—Are the Future Cloud
- Non-Linux FOSS: libnotify, OS X Style
- Linux Systems Administrator
- Validate an E-Mail Address with PHP, the Right Way
- Lock-Free Multi-Producer Multi-Consumer Queue on Ring Buffer
- Senior Perl Developer
- Technical Support Rep
- UX Designer
- RSS Feeds
- Introduction to MapReduce with Hadoop on Linux
Featured Jobs
| Linux Systems Administrator | Houston and Austin, Texas | Host Gator |
| Senior Perl Developer | Austin, Texas | Host Gator |
| Technical Support Rep | Houston and Austin, Texas | Host Gator |
| UX Designer | Austin, Texas | Host Gator |
| Web & UI Developer (JavaScript & j Query) | Austin, Texas | Host Gator |
Free Webinar: Hadoop
How to Build an Optimal Hadoop Cluster to Store and Maintain Unlimited Amounts of Data Using Microservers
Realizing the promise of Apache® Hadoop® requires the effective deployment of compute, memory, storage and networking to achieve optimal results. With its flexibility and multitude of options, it is easy to over or under provision the server infrastructure, resulting in poor performance and high TCO. Join us for an in depth, technical discussion with industry experts from leading Hadoop and server companies who will provide insights into the key considerations for designing and deploying an optimal Hadoop cluster.
Some of key questions to be discussed are:
- What is the “typical” Hadoop cluster and what should be installed on the different machine types?
- Why should you consider the typical workload patterns when making your hardware decisions?
- Are all microservers created equal for Hadoop deployments?
- How do I plan for expansion if I require more compute, memory, storage or networking?




14 min 58 sec ago
2 hours 43 min ago
3 hours 16 min ago
3 hours 17 min ago
3 hours 18 min ago
3 hours 20 min ago
3 hours 21 min ago
3 hours 23 min ago
3 hours 24 min ago
3 hours 25 min ago