Witness the development of H.265

Future Collaboration between MPEG and VCEG

2009-07-03 H.265/HEVC 3 Comments Views(8,252)

As well-known, JVT (a joint video team between MPEG and VCEG) has achieved great success in the standardization of the state-of-the-art video compression, including H.264/AVC, SVC, and MVC. The academic and industry want to continue the success in the collaboration between MPEG and VCEG. Some proposals from VCEG suggest the H.NGVC (or H.265) standardization should be collaboratively done with MPEG to produce a significantly advanced video coding standard and to avoid producing multiple similar standards, which confuse the market. VCEG agrees that conducting this work jointly with MPEG will be beneficial to avoid duplication of efforts in this area. Therefore, in this MPEG London meeting, a joint meeting between MPEG and VCEG discusses the future collaboration. However, the collaboration method arouses a lot of controversy, focusing on which one of the following practical experiences should be chosen:

  • Practical experience 1: MPEG-2, a integrate part of video subgroup when under WG 11 a[......]

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Current Status of HVC (High-Performance Video Coding) in MPEG

2009-07-03 H.265/HEVC 11 Comments Views(20,218)

In the last MPEG meeting, MPEG issued a Call for Evidence (CfE) on High-performance Video Coding (HVC). Nine responses to the CfE are received in this meeting (89th MPEG London). Those reponse proposals adopt typical coding tools in KTA, such as adaptive loop filter (ALF), extended macroblock size (EMS), larger transform size (LTS), internal bit depth increasing (IBDI), adaptive quantization matrix selection (AQMS), as well as new tools, such as modified intra prediction, modified de-block filter, decoder-side motion vector derviation (DMVD).
The objective experimental results show that 20% average bit reduction is achieved compared with H.264/AVC High Profile for all classes of test video sequences (Class A: 19%, Class B:25%, Class C:22%, Class D: 15% bit reductions, respectively). Subjective evalution is also conducted during this meeting. The purpose of subjective evaluation is identifying examples that give the best evidence and assessing whether the evidence is large enough. The s[......]

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Quantization Techniques in JM/KTA – Part 4

2009-06-21 KTA Post Comment 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+ 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
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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Quantization Techniques in JM/KTA – Part 3

2009-06-21 KTA 2 Comments 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 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.,
Any values in an interval would be reconstructed to some W’, so the distribution of W’ is a probability ma[......]

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Quantization Techniques in JM/KTA – Part 2

2009-06-21 KTA Post Comment Views(7,454)

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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Quantization Techniques in JM/KTA – Part 1

2009-06-21 KTA Post Comment 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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R-D Performance of 1/8-pel MCP on HD Sequences

2009-06-07 KTA 7 Comments Views(5,314)

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.
Frequency response
  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.
Performance of 1/8-pel MCP

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Transform Error Introduced by Non-orthogonality

2009-04-27 KTA 5 Comments Views(7,184)

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.
The transformation matrix of the proposed 2D order-16 transform
  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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