Advanced binary arithmetic decoding algorithm for H.264/AVC

In Section 2.2.2.1, we introduce the basic algorithm of the binary arithmetic decoder. Although it can achieve the high compression gain, the algorithm works under the floating-point operation. The hardware complexity becomes the problem when we implement the binary arithmetic decoder. In Eq. 1, it has to compute the values of rMPS and rLPS with two multipliers and processes the next value of codlOffset, range, and the probability by means of the floating adders and comparators. It consumes the lots hardware cost because the multipliers and floating operations make the complex circuit. According to H.264/AVC standard [1], we adopt the low complexity algorithm to implement the CABAD circuit.

In order to improve the coding efficiency, there are three kinds of the binary arithmetic decoders in H.264/AVC system such as the normal, bypass, and termination decoding flow. We will show whole algorithms as follows.

The first algorithm is the normal decoding process which is shown in Figure 8.

There are two main factors to dominate the hardware efficiency. One is the multiplier of range×ρ_LPS defined as rLPS and the other is the probability calculation defined asρ_LPS. In Eq. 1, it applies one multiplier to find the range of LPS (rLPS). According to the H.264/AVC standard, the table-based method is used in place of the multiplication operation. In the normal decoding flowchart, codlRangeLPS looks up the table, rangeTabLPS, depending on two indexes such as pStateIdx and qCodlRangeIdx. pStateIdx is defined as the probability of MPS (ρ_MPS) which gets from the context model. qCodlRangeIdx is the quantized value of the current range (codlRange) which is separated to four parts in this table. The second factor of the improved method is to estimate the value of ρ_MPS. In Section 2.2.2.1, we know that the value of ρ_MPS is increased when MPS condition happened and is decreased when LPS condition happened. But we can’t find the rule how much the value has to

be increased or decreased. The flowchart of Figure 8 also shows the table-based method to process the probability estimation. It divides into two sub-intervals such as MPS and LPS conditions. Depending on the sub-interval, it computes the next probability by the transIdxLPS table when the interval division is LPS and by the transIdxMPS table when the interval is MPS. These two probability tables are approximated by sixty-four quantized values indexed by the probability of the current interval.

codIOffset >= codIRange

binVal = !valMPS codIOffset = codIOffset - codIRange

codIRange = codIRangeLPS

qCodIRangeIdx = (codIRange>>6) & 3 codIRangeLPS = rangeTabLPS[pStateIdx][qCodIRangeIdx]

codIRange = codIRange - codIRangeLPS

pStateIdx == 0?

valMPS = 1 - valMPS

pStateIdx = transIdxLPS[pStateIdx]

Yes

No

Figure 8 the flowchart of the normal decoding flow [1]

In the basic binary arithmetic decoder, the interval subdivision is operated under the floating-point operation. In practical implementation, this method causes the complexity of the circuit to be increased. The advanced algorithm adopts the integer operation for H.264/AVC. The value of the next range becomes smaller than the current interval. So we use the renormalization method to keep the scales of codlRange and codlOffset. Figure 9 shows the flowchart of renormalization. The MSB of codlRange always keeps “1” in order to realize the integer operation. If the

MSB of codlRagne is equal to “0”, the value of codlRagne has to be shifted left until the current bit is equal to “1”. Depending on the shifted number of codlRagne, codlOffset fill the bit-stream in the LSB.

Figure 9 Flowchart of renormalization [1]

The second algorithm is the bypass decoding process which is applied by the specified syntax elements such as abs_mvd, significant_coeff_flag, last_significant_coeff_flag, and coeff_abs_level_minus1. The probabilities of MPS and LPS are fair, that is, both probabilities are 0.5. It is unnecessary to refer to the context model during decoding. Figure 10 shows the flowchart of the bypass decoding flow. Compared with Figure 8, the bypass decoding process doesn’t estimate the probability of the next interval. So we can’t see the probability computation in the bypass decoding. The computed codlRange doesn’t change which means that it has no renormalization in the bypass decoding. It just uses one subtraction to implement this decoding process. This algorithm is very simple, so we will use the architecture to speed up the CABAD system.

Figure 10 Flowchart of the bypass decoding flow [1]

codIOffset >= codIRange

binVal = 1 binVal = 0

Yes No

Done DecodeTerminate

RenormD codIRange = codIRange-2

Figure 11 Flowchart of the terminal decoding flow [1]

The third algorithm is the termination decoding process. Figure 11 show the flowchart of the terminal decoding flow. The terminal decoding process is very simple as well, but it has the more decoding procedure compared to the bypass decoding process. It doesn’t need the context model to refer to the probability. The value of the next codlRange is always to subtract two from the current codlRange depending on whether the subdivision condition belongs to MPS or not. The final values of codlRange and codlOffset are required to renormalize through the RenormD in this figure when it branches to the situation that codlOffset is smaller than codlRange (MPS condition). The architecture of this flowchart is composed of one constant subtraction, one comparator, and one renormalization. The termination decoding process is used to trace if the current slice is ended. It occurs one time per macroblock process which is seldom used during all decoding processes.

The efficiency, therefore, affects slightly in CABAD system. So we will focus on the first two algorithms in this section.