U. Glaeser

FIGURE 42.26 Sequence of zigzag-coding coefficients of a quantised 8 × 8 block.
Quantization
Quantization is a process that maps the symbols representing the DCT transformed coefficients from
one set of levels to a narrower one in order to minimise the number of bits required to transmit the
symbols. Quantization in block-based coders is a lossy process and thus it has a negative impact on the
perceptual quality of the reconstructed video sequence. The quantization parameter (Qp) is a user-defined
parameter that determines the level of distortion that affects the video quality due to quantization. The
higher the quantization level, Qp, the coarser the quantization process. Quantization uses different techniques
based on the coding mode employed (INTRA or INTER), the position of the coefficient in a video
block (DC or AC coefficients), and the coding algorithm under consideration.
Raster Scan Coding
It is also known as zigzag pattern coding. The aim of zigzag coding the 8 × 8 matrix of quantised DCT
coefficients is to convert the two-dimensional array into a stream of indices with a high occurrence of
successive 0 coefficients. The long runs of zeros will then be efficiently coded as will be shown in the
next subsection. The order of a zigzag pattern encoder is depicted in Fig. 42.26.
Run-Length Coding
The run-length encoder takes the one-dimensional array of quantised coefficients as input and generates
coded runs as output. Instead of coding each coefficient separately, the run-length coder searches for runs
of similar consecutive coefficients (normally zeros after the DCT and quantisation stages) and codes the
length of the run and the preceding nonzero level. A 1-bit flag (LAST) is sent after each run to indicate
whether or not the corresponding run is the last one in the current block. Run-lengths and levels are then
fed to the Huffman coder to be assigned variable length codewords before transmission on the video channel.
Huffman Coding
Huffman coding, traditionally referred to as entropy coding, is a variable length coding algorithm that
assigns codes to source-generated bit patterns based on their frequency of occurrence within the generated
bit stream. The higher the likelihood of a symbol, the smaller the length of the codeword assigned to it
and vice versa. Therefore, Entropy coding results in the optimum average codeword size for a given set
of runs and levels.
Motion Estimation and Prediction
For each MB in a currently processed video frame, a sum of absolute differences (SAD) is calculated
between its pixels and those of each 16 × 16 matrix of pixels that lie inside a window (in the previous
frame) of a user-defined size called the search window. The 16 × 16 matrix, which results in the least
© 2002 by CRC Press LLC