I think I'll post something new I learned every week (mostly weekends). Here goes the first. This is about Video Encoding. Here are somethings I learned in the process of research.
Sources:
Quantization (Image Processing)
HOWTO Mencoder Introduction Guide
Webopedia
Video codecs and formats are not the same thing. MPEG 4 for e.g. is a video format, Xvid is a codec. Codecs create the actual videos.
Then there are multimedia containers. The container is what will contain the encoded video and audio. You can put anything into the container format (as long as it supports it - e.g. video and audio). One e.g. of a container format is AVI.
Some programs to do video encoding:
- VirtualDub (gui + commandline)
- mencoder (commandline)
Some terminology/concepts useful when using these programs:
- Quantization: a lossy compression technique achieved by compressing a range of values to a single quantum value.
- I-frame: also known as the Key frame, these frames contain information frame information without reference to any other frames (think of it as 1 snapshot in a movie; this will make more sense as you read P-frame and B-frame(s) below). Hence I-frames take the most bits to store, but improve the video quality
- P-frame: P-frames follow I-frames and contain information that has changed since that I-frame (such as color information and content change). Hence, they depend on the I-frame to fill in their data. P-frames are also aptly called delta-frames.
- B-frame: B-frames or bi-directional predictive frames rely on the frames preceeding and following them. They contain data of what has changed between the 2 frames.
- GOP: stands for Group of Pictures
The [I/P/B]-frame quantization values range between 1-31. The higher the number, the more compression (hence more loss of information --> smaller file size --> lower quality)
Bitrate is how much bits per second to store the data (higher means more bits are used to store per second of data, meaning more information stored).
Based on the above 2 factors (at least at a basic level), the quality and file size of videos can be controlled. The challenge is to find the right values that would optimize the quality to fit your space needs (possibly time needs as well, i.e. how long you have to encode)
Of course, one thing to be remembered is the encode's quality would depend on the source's quality. It wouldn't matter if you had quantization values of 1 and bitrate of 2000Kbps if the source is very poor)
Also, there are other options you can play with that would have an effect in the quality and time taken like motion detection etc..but think of this as a basic starting point
)[ add comment ] | [ 0 trackbacks ] | permalink |
( 2.8 / 24 )Time to upgrade
[ add comment ] | [ 0 trackbacks ] | permalink |
( 3 / 66 )I started searching about Business Intelligence...this slowly led to other things...one of which, was data warehousing. I have quoted one part from Wikipedia, which I found easy to understand:
Source: http://en.wikipedia.org/wiki/Data_warehouse
In OLTP — online transaction processing systems relational database design use the discipline of data modeling and generally follow the Codd rules of data normalization in order to ensure absolute data integrity. In this approach, each of the more complex information items is resolved into a set of records in multiple tables, each of which satisfies the normalization rules. Codd defines 5 increasingly stringent rules of normalization and typically OLTP systems achieve a 3rd level normalization. Fully normalized OLTP database designs often result in having information from a business transaction stored in dozens to hundreds of tables. Relational database managers are efficient at managing the relationships between tables and result in very fast insert/update performance because only a little bit of data is affected in each relational transaction.
OLTP databases are efficient because they are typically only dealing with the information around a single transaction. In reporting and analysis, thousands to billions of transactions may need to be reassembled imposing a huge workload on the relational database. Given enough time the software can usually return the requested results, but because of the negative performance impact on the machine and all of its hosted applications, data warehousing professionals recommend that reporting databases be physically separated from the OLTP database.
In addition, data warehousing suggests that data be restructured and reformatted to facilitate query and analysis by novice users. OLTP databases are designed to provide good performance by rigidly defined applications built by programmers fluent in the constraints and conventions of the technology. Add in frequent enhancements, and too many a database is just a collection of cryptic names, seemingly unrelated and obscure structures that store data using incomprehensible coding schemes; all factors that while improving performance, complicate use by untrained people. Lastly, the data warehouse needs to support high volumes of data gathered over extended periods of time and are subject to complex queries and need to accommodate formats and definitions inherited from independently designed package and legacy systems.
Designing the data warehouse data Architecture synergy is the realm of Data Warehouse Architects. The goal of a data warehouse is to bring data together from a variety of existing databases to support management and reporting needs. The generally accepted principle is that data should be stored at its most elemental level because this provides for the most useful and flexible basis for use in reporting and information analysis.
Also this is on data mining,
Source: http://searchsqlserver.techtarget.com/s ... 01,00.html
Data mining is sorting through data to identify patterns and establish relationships.
Data mining parameters include:
* Association - looking for patterns where one event is connected to another event
* Sequence or path analysis - looking for patterns where one event leads to another later event
* Classification - looking for new patterns (May result in a change in the way the data is organized but that's ok)
* Clustering - finding and visually documenting groups of facts not previously known
* Forecasting - discovering patterns in data that can lead to reasonable predictions about the future (This area of data mining is known as predictive analytics.)
Now, flowing down to the real deal - Business Intelligence:
Source: http://searchdatamanagement.techtarget. ... 71,00.html
Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining.
[ add comment ] | [ 0 trackbacks ] | permalink |
( 2.6 / 77 )Keywords:
* TSO
* JCL
* ESP
* SAS
* Data set(IBM Mainframe)
* BIA
* Mainframe
[ add comment ] | [ 0 trackbacks ] | permalink |
( 3.1 / 40 )Saving this for a later read
Seems like a good article, I feel too tired to read at the moment
, I'll just post the link here for now:http://iptables-tutorial.frozentux.net/ ... orial.html
[ add comment ] | [ 0 trackbacks ] | permalink | related link |
( 2.8 / 40 )
Calendar
