Video Coding Techniques
Video encoding and decoding involve the digital transformation from a video signal, typically the audio data, into a computer program (an encoder) and the decoding of this encoded video signal back into an audio signal (decoder). The Encoder and Decoder is also called a Videooder and Decoder. The Encoder and Decoder is used to compress video signals, which are then compressed into a video file. The compression techniques which are used on the Encoder and Decoder have been classified in four areas – Compression Using BZip2, Fixed-length Codebooks, Progressive Scan, and DeFLux.
The compression techniques used on the Encoder and Decoder are also classified in four areas – Time compression, locality compression, greedy compression, and BZip2. The Time compression algorithm is a modification of the bzip2 algorithm. The Time compression technique is suitable for low bandwidth systems. It compresses the video signals into small packets which can be transmitted over long distances. The locality compression technique makes the encoded stream more sparse in its representation and is therefore suited for low bandwidth systems.
The main challenge in video coding standards development is to combine the various compression efficiency techniques so that the end result is the highest achievable video compression efficiency. As is well known, the compression efficiency of any encoding method depends not only on the type of compression applied but also on the characteristics of the input signal. In order to meet certain compression efficiencies, some techniques employ advanced video coding techniques which have not yet been fully explored.
One of the advanced video coding techniques is the context compression algorithm. This algorithm performs image transformations using the X-ray transform and the super-fast principle. The super-fast principle is a key component in the context transform because it makes use of the fast image sampling. The X-ray transform allows the digital video encoder to perform context appropriate transformations without the requirement for a fast camera response time.
The second important video coding techniques is the discrete cosine transform (DCAT) and the Bicramatic algorithm. The discrete cosine transform (DCAT) can be implemented as an arithmetic video coding technique using a polynomial equation. The Bicramatic algorithm first implements the Bicramatic formula which is based on the discrete cosine transform. This algorithm was developed by Arthur Conway and Alva loop who are credited with the formulation of the algorithm.
The next video coding techniques we shall discuss is the motion estimation process. Motion estimation is also known as the displacement estimation or the image motion estimation. This technique makes use of the computed image of the scene in order to extract the motion vector from the data. A motion estimation algorithm makes use of either the orthogonal transformation matrix, the bicramatic formula or the closed-form formula. For more details, you can refer to the other articles in this series.
For the Bicramatic algorithm, a set of images is sliced by the quadratic formula into four components. After slicing, the resultant set of images is then processed through the open loop. This process is called the motion compensation. When the processed image is then passed through the high pass filter, the resulting video coding is called as the Bicramatic motion estimation.
The last video coding technique we will be discussing is the use of the entropy coding stage. The entropy coding makes use of random numbers to encode the encoded video signal. However, certain limits can be defined as the range of the random numbers. Generally, the range of the chosen number is between zero and one. However, the range of the chosen number must be such that the decoded signal will have quality degradation. Then, the entropy coding stages are performed in order to bring the final result in the final format where the video signal will be at the quality level we want.