3. Adaptive Loop Filter

As far as adaptive loop filter (ALF) is concerned, there are three types of ALF: frame-based, block-based and quadtree-based ALFs. All of them are based on wiener filter, but with different filtering control basis. In frame-based ALF [VCEG-C437/AI14, C402], only one picture level flag is used to signal the decision of filtering or non-filtering.

Although wiener filter can restore the reconstructed picture to the original picture globally, there are degraded pixels locally. Since the degraded area reduce the filtering efficiency, if these areas are not filtered, the capabilities of picture restoration and loop filtering are improved. Therefore, block-based ALF [VCEG-AI18/AJ13] use explicit flags for filtering on-off on block by block basis, while quadtree-based ALF [VCEG-C181/AK22] introduces a quadtree data structure to carry out the variable-size block filtering.

3.1 Block-based Adaptive Loop Filter

Block-based ALF is an improvement of frame-based ALF. Figure 2[......]

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Permanent Link: Adaptive Post/Loop Filters in JM/KTA – Part 2

1. Introduction

The basic idea of adaptive post/loop filter is the same. Both of them use adaptive wiener filtering technique to improve the quality of reconstructed picture which is degraded by compression. The difference between them is whether the filtering process is applied in or out of the core coding loop, as shown in Figure 1,  to improve the quality of reconstructed picture or just displayed picture.

Figure 1. Block diagram of JM/KTA

2. Adaptive Post Filter

In H.264/AVC, there is already an existing post-filter hint SEI message [JVT-S030/T039/U035] which provides the coefficients of a post-filter or correlation information for the design of a post-filter for potential use in post-processing of the output decoded pictures to obtain improved displayed quality.

To find the coefficients of adaptive wiener filter, the following cost function based on the whole frame is minimized:

(1)

where R is the reconstructed picture, R’ is the filtered picture, and I is the original pic[......]

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Permanent Link: Adaptive Post/Loop Filters in JM/KTA – Part 1

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+ λR 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

                                                   (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 – Part 4

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 λ, 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 |W’|, i.e.,

                                                                                          (5)

Any values in an interval would be reconstructed to some W’, so the distribution of W’ is a probability ma[......]

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Permanent Link: Quantization Techniques in JM/KTA – Part 3

2. Principle of H.264/AVC Normal Quantization Scheme

2.1. Scalar dead-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 W’=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 – Part 2

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 – Part 1

The technique of 1/8-pel 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 for 1/4-pel sub positions.
• Bilinear filter for 1/8-pel sub positions.

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

  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], i.e., PSNR improvement at the same bit-rate or bit-rate reduction at the same PSNR.

 

 

 [......]

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

In the Geneva meeting held in Feb. 2009, a proposal with the title “Video Coding Using Extended Block Sizes” was adopted by KTA, where the MB size is extended up to 64×64 and the motion partitions are scaled accordingly. At the same time, a 2D order-16 transform was also proposed for transforming the residual blocks with the size larger than or equal to 16×16. The transformation matrix of the proposed 2D order-16 transform is given as below, which is obtained by scaling the transformation matrix of 2D order-16 DCT by the factor 128 and rounding, and is non-orthogonal.

  Non-orthogonality will inevitably introduce transform error. Before analyzing the transform error quantitatively, let’s recall two properties of orthogonal transforms. Firstly, signals can be reconstructed perfectly if no quantization is performed in the transform domain. Secondly, if quantization is performed in the transform domain, the average variance (or energy) of the reconstruction er[......]

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Permanent Link: Transform Error Introduced by Non-orthogonality