2009-06-21
Yu Liu
KTA
Views(9,506)
**4. ****Rate-Distortion Optimized Quantization**

Previously, adaptive rounding was proposed to improve quantization, which captures the statistics of the incoming residual signal and adjusts the rounding offsets accordingly. However, the adaptive rounding quantization is still based on the criterion which minimizes the mean-squared quantization error between the original signal and the quantization reconstructed signal. From the sense of rate-distortion optimization, the cost from the rate should also be considered.

The basic idea underlying the rate-distortion optimized quantization is to minimize a cost function *D+ Î»R* such that both the rate R and the distortion D are considered in coding decisions. For quantization case, the RD optimal coding is to solve a minimization problem of

Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â (7)

where *S* is the original signal, and *T-1* denotes the inverse transform operation. Consider that the DCT is a unitary transform, which maintains the Euclidean d[......]

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Permanent Link: Quantization Techniques in JM/KTA â€“ Part 4

2009-06-21
Yu Liu
KTA
Views(5,782)
**3. ****Adaptive Rounding Encoding Technique using an Equal Expected-Value Rule**

As discussed above, if the input p.d.f. is Laplacian distributed and if we can estimate *Î»*, then the optimal *f* can be found analytically. But, usually the estimate of input p.d.f. is not available, then, how to select the rounding offset *f*?

In order to select rounding offset *f* adaptively, an adaptive quantization encoding technique using an equal expected-value rule is proposed by Gary Sullivan from Microsoft. The adaptive adjustment of the rounding offset *f *occurs only in the *encoding* quantization process, which tries to select *f* without using any priori model knowledge on the input *W*. The aim is to make that the mean of the absolute value of the input, |*W*|, is equal to its expected reconstruction value |*Wâ€™*|, i.e.,

Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â Â (5)

Any values in an interval would be reconstructed to some *Wâ€™*, so the distribution of *Wâ€™* is a probability ma[......]

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Permanent Link: Quantization Techniques in JM/KTA â€“ Part 3

2009-06-21
Yu Liu
KTA
Views(7,454)
**2. ****Principle of H.264/AVC ****Normal**** Quantization Scheme**

**2.1. Scalar d****ead-zone quantization**

In this section the principle of H.264/AVC normal quantization scheme is described in a generalized form.

A scalar quantizer for input signal *W* can be decomposed into a function *Z=C[W]* called a classification rule that selects an integer-valued class identifier called the quantization index at the encoder, and a reconstruction rule that produces a real-valued output *Wâ€™=R[Z]* at the decoder. Video encoder applies entropy coding to the quantization indices and communicates to the decoder. Although H.264/AVC JM reference software implements some classification functions, only reconstruction function is standardized.

In the quantization step of the encoder, the transform coefficients of the prediction error are quantized. This quantization is used to reduce the precision of the coefficients. Furthermore, the quantizer is designed to map insignificant coefficient values to zero whilst retaining a reduced [......]

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Permanent Link: Quantization Techniques in JM/KTA â€“ Part 2

2009-06-21
Yu Liu
KTA
Views(15,030)
**1. Overview**

Currently most image and video coding systems and standards, such as MPEG-1/2 and H264/AVC, use transform-based techniques followed by quantization and entropy coding. The key idea is that transforms de-correlate the signal and compact the energy of a block into a few coefficients, which still represent the signal rather accurately after quantization and de-quantization. Nevertheless, this quantization/de-quantization process needs to be carefully designed in order to have the best possible subjective and objective quality.

In the encoder of H.264/AVC reference software, the scalar dead-zone quantization is adopted. In order to improve further the performance, other two adaptive quantization techniques are also introduced, which are both based on how to adjust the size of dead-zone and control the rounding behavior. In this tutorial, we will first introduce the principle of H.264/AVC normal quantization scheme, then discuss the adaptive rounding method which select adaptive[......]

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Permanent Link: Quantization Techniques in JM/KTA â€“ Part 1

2009-06-07
Jie Dong
KTA
Views(5,314)
The technique of 1/8-pelÂ interpolation [AD09] was proposed for motion-compensated prediction (MCP) and adopted in KTA software. Three types of interpolation filters are used for 1/2-, 1/4-, and 1/8-pel sub positions, respectively.

- [-3, 12, -39, 158, 158, -39, 12, -3]/256 for 1/2-pel sub positions.
- [-3, 12, -37, 229, 71, -21, 6, -1]/256 and [-1, 6, -21, 71, 229, -37, 12, -3]/256 forÂ 1/4-pel sub positions.
- Bilinear filter for 1/8-pel sub positions.

Â The frequency response of the interpolation filterÂ is shown in the following figure.Â As can be seen,Â it is almost an ideal low-pass filter with a gain of 8 and a cutoff frequency Ï€/8.

Â According to the performance reported in the proposal, the gain on CIF/QCIF sequences is quite significant, i.e., up to 14% bit-rate reduction. I tested this technique based on a set of HD sequences. As shown in Table 1, the R-D performance is measured by BDPSNR [1],Â i.e., PSNRÂ improvement at the same bit-rate or bit-rate reduction at the same PSNR.

Â

Â

Â [......]

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Permanent Link: R-D Performance of 1/8-pel MCP on HD Sequences