logo

English

How video compression works 2/5 - Quantization, coding, and prediction

by digipine posted Nov 02, 2017
?

Shortcut

PrevPrev Article

NextNext Article

Larger Font Smaller Font Up Down Go comment Print Attachment
?

Shortcut

PrevPrev Article

NextNext Article

Larger Font Smaller Font Up Down Go comment Print Attachment

Quantization, coding, and prediction 

Choosing the bits: quantization and coding
After the block transform is performed, the transform coefficients for each block are compressed using quantization and coding. Quantization reduces the precision of the transform coefficients in a biased manner: more bits are used for low-frequency coefficients and fewer bits for high-frequency coefficients. This takes advantage of the fact, as noted above, that human vision is more sensitive to low-frequency information, so the high-frequency information can be more approximate. Many bits are discarded in this step. For example, a 12-bit coefficient may be rounded to the nearest of 32 predetermined values. Each of these 32 values can be represented with a five-bit symbol. In the decompression algorithm, the coefficients are "dequantized"; i.e., the five-bit symbol is converted back to the 12-bit predetermined value used in the encoder. As illustrated in Figure 3, the dequantized coefficients are not equal to the original coefficients, but are close enough so that after the inverse transform is applied, the resulting image contains few or no visible artifacts.

 

 b03ecd45ee1305761fd63759f1cc7d94.gif

 


In older video algorithms, such as MPEG-2, dequantization can require anywhere from about 3% up to about 15% of the processor cycles spent in a video decoding application. Cycles spent on dequantization in modern video algorithms (such as H.264) are negligible, as are the memory requirements. 

Statistically Speaking
Next, the number of bits used to represent the quantized DCT coefficients is reduced by "coding," which takes advantage of some of the statistical properties of the coefficients. After quantization, many of the DCT coefficients—often, the vast majority of the high-frequency coefficients—are zero. A technique called "run-length coding" takes advantage of this fact by grouping consecutive zero-valued coefficients (a "run") and encoding the number of coefficients (the "length") instead of encoding the individual zero-valued coefficients. 


Run-length coding is typically followed by variable-length coding (VLC). In variable-length coding, commonly occurring symbols (representing quantized DCT coefficients or runs of zero-valued quantized coefficients) are represented using code words that contain only a few bits, while less common symbols are represented with longer code words. By using fewer bits for the most common symbols, VLC reduces the average number of bits required to encode a symbol thereby reducing the number of bits required to encode the entire image.

On the decompression side, variable-length decoding (VLD) reverses the steps performed by the VLC block in the compression algorithm. Variable-length decoding is much more computationally demanding than variable-length coding. VLC performs one table lookup per symbol (where a symbol is encoded using multiple bits); in contrast, the most straightforward implementation of VLD requires a table lookup and some simple decision making to be applied for each bit. VLD requires an average of about 11 operations per input bit. Thus, the processing requirements of VLD are proportional to the video codec's selected bit rate. VLD can consume as much as 25% of the cycles spent in a video decoder implementation.

In a typical video decompression algorithm, the straightforward VLD implementation described above (which operates on one bit at a time) requires several kilobytes of lookup table memory. It is possible to improve the performance of the VLD by operating on multiple bits at a time, but this optimization requires the use of much larger lookup tables.

Some of the newer standards (such as H.264) replace or augment the run-length coding and VLC techniques described above to achieve greater compression. For example, H.264 supports both CAVLC (context-adaptive VLC) and CABAC (context-adaptive arithmetic coding). CAVLC augments VLC by adapting the coding scheme based on previously-coded coefficients. CABAC replaces VLC entirely, using instead a more efficient—but also more computationally demanding—scheme of arithmetic coding. CABAC can consume as many as 50% of the cycles in an H.264 decoder.

Looking at a bigger picture
All of the techniques described so far operate on each 8x8 block independently from any other block. Since images typically contain features that are much larger than an 8x8 block, more efficient compression can be achieved by taking into account the similarities between adjacent blocks in the image.


To take advantage of inter-block similarities, a prediction step is often added prior to quantization of the transform coefficients. In this step, codecs attempt to predict the image information within a block using the information from the surrounding blocks. Some codecs (such as MPEG-4) perform this step in the frequency domain, by predicting DCT coefficients. Other codecs (such as H.264) do this step in the spatial domain, and predict pixels directly. The latter approach is called "intra prediction."

In this step, the encoder attempts 

 
TAG •

List of Articles
No. Subject Author Date Views
67 TI사의 DSP 종류 digipine 2017.11.02 766
66 How video compression works 1/5 file digipine 2017.11.02 1596
» How video compression works 2/5 - Quantization, coding, and prediction file digipine 2017.11.02 5829
64 How video compression works 3/5 - Color and motion file digipine 2017.11.02 1595
63 How video compression works 4/5 - Encoder secrets and motion compensation digipine 2017.11.02 340
62 How video compression works 5/5 - Deblocking, deringing, and color space conversion file digipine 2017.11.02 23694
61 임베디드 무선랜 개발 iwconfig wpa_supplicant digipine 2017.11.02 775
60 Linux Wi-Fi Setup Tools and Commands digipine 2017.11.02 15446
59 서보 모터 (Servo-motor) 의 내부 구조와 회전 원리 file digipine 2017.11.02 807
58 서보모터의 기초와 제어 file digipine 2017.11.02 422
57 차세대 비휘발성 메모리 기술동향 file digipine 2017.11.02 247
56 반도체 장비/공정 기술 용어집 digipine 2017.11.02 1888
55 반도체 전공정 후공정 설명 digipine 2017.11.02 4304
54 SECS 프로토콜 개요 digipine 2017.11.03 893
53 RFID와 USN 에 대해서 digipine 2017.11.03 226
52 DaVinci 에 대한 소개글 digipine 2017.11.03 112
51 L4 장비의 핵심 로드 밸런싱(Load Balancing) digipine 2017.11.03 946
50 네트웍 용어중 bps, cps, BPS, pps 의 차이점 digipine 2017.11.03 1576
49 UC 환경을 위한 종합 네트워크 FMC digipine 2017.11.03 202
48 CISCO Router 설정 팁 - QOS 1 digipine 2017.11.03 1816
Board Pagination Prev 1 2 3 ... 4 Next
/ 4