Video is one of the stimulating areas in electronic communications and multimedia applications. Video can do great things to enhance a presentation, illustration, or advertise a new product. Video files are photographic images played at speeds that make it appear to the human eyes as if its images or frames are in full motion. It does not wonder that the video files can be extremely large because the number of images required to give appearance of motion. A single second of uncompressed video running at 30 frames per second may require more than 30 Mega bytes of storage space.

The other challenge is how to deliver a video file over the internet with small storage and fast speed, eventhough the internet bandwith is low. In order to be used effetively, however, video is often compressed for storageand transfer, and then decompressed for use.

This research will find the meeting of two conflicting requirements, reducing the transmission bit-rate and increasing the image quality. Compression depends on two factors : (1) motion estimation – a process of estimating the pixels of the current frame from the reference frame, and (2) motion compensation – a process for the residual error after motion estimation. This research will develop a low bit-rate video coding system for video-telephone, video conferencing, and video streaming to mobile phones, which have limited processing and bandwith capacity.

The research goals are : Low bit-rate video coding algorithm focusing on moving region, apply error surface corelation in video coding, applying artificial neural network in presenting arbitrary shaped moving objects, and applying wavelet transformation algorithm to compress the video file and improve the quality of the iamges.

Applications of this research are studio-based and desktop video conferencing, surveillance and monitoring, telemedicine, computer based training, video phone, and video over internet.

There are 7 steps in how planning research :

1. Finding a problem
2. Formulating a model
3. Devising testable predictions
4. Comparative studies
5. Interrelationship between field and laboratory
6. Other aspects of planning research
7. Implementing the plan

In order to plan research effectively, the investigator should understand how his or her activities fit into the endeavor of science as a whole. Some explanations of the ‘‘scientificmethod’’ confound epistemology –
how we accumulate knowledge and understanding through science – with specific research activities of the individual investigator. This section attempts to disentangle the two by sketching the ‘‘big picture’’ first and then showing where the practicing scientist fits in.

Secara garis besar, Kecerasan Buatan (Artifficial Intellegence / AI) terbagi ke dalam dua faham pemikiran yaitu AI Konvensional dan Kecerdasan Komputasional (CI, Computational Intelligence). AI konvensional kebanyakan melibatkan metoda-metoda yang sekarang diklasifiksikan sebagai pembelajaran mesin, yang ditandai dengan formalisme dan analisis statistik. Dikenal juga sebagai AI simbolis, AI logis, AI murni dan AI cara lama (GOFAI, Good Old Fashioned Artificial Intelligence). Metoda-metodanya meliputi:

1. Sistem pakar: menerapkan kapabilitas pertimbangan untuk mencapai kesimpulan. Sebuah sistem pakar dapat memproses sejumlah besar informasi yang diketahui dan menyediakan kesimpulan-kesimpulan berdasarkan pada informasi-informasi tersebut.
2. Pertimbangan berdasar kasus
3. Jaringan Bayesian
4. AI berdasar tingkah laku: metoda modular pada pembentukan sistem AI secara manual

Kecerdasan komputasional melibatkan pengembangan atau pembelajaran iteratif (misalnya penalaan parameter seperti dalam sistem koneksionis. Pembelajaran ini berdasarkan pada data empiris dan diasosiasikan dengan AI non-simbolis, AI yang tak teratur dan perhitungan lunak. Metoda-metoda pokoknya meliputi:

1. Jaringan Syaraf: sistem dengan kemampuan pengenalan pola yang sangat kuat
2. Sistem Fuzzy: teknik-teknik untuk pertimbangan di bawah ketidakpastian, telah digunakan secara meluas dalam industri modern dan sistem kendali produk konsumen.
3. Komputasi Evolusioner: menerapkan konsep-konsep yang terinspirasi secara biologis seperti populasi, mutasi dan “survival of the fittest” untuk menghasilkan pemecahan masalah yang lebih baik.

Metoda-metoda ini terutama dibagi menjadi algoritma evolusioner (misalnya algoritma genetik) dan kecerdasan berkelompok (misalnya algoritma semut)