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### 细粒度可伸缩性（Fine Granularity Scalability, FGS）在MPEG-4视频标准中的概述 #### 引言随着互联网技术的发展，流媒体视频应用的需求日益增长，传统视频编码方法已无法满足网络视频传输的灵活性与高效性需求。为此，MPEG组织推出了针对流媒体视频的扩展——MPEG-4 Streaming Video Profile，该扩展旨在为通过互联网进行视频流传输提供支持。此扩展不仅提供了高效的单层帧基视频分发能力，还引入了细粒度可伸缩性（Fine Granularity Scalability, FGS），以应对互联网视频传输过程中的各种挑战。 #### 细粒度可伸缩性概述细粒度可伸缩性（FGS）是一种视频编码技术，它允许在编码过程中将视频信号按照不同的质量等级进行分割。这种特性使得视频可以在不同比特率下灵活地传输，从而适应网络带宽的变化。FGS不仅可以单独使用，还可以与时间可伸缩性（Temporal Scalability）相结合，进一步提高视频传输的适应性和效率。 #### 关键技术要点 1. **比特平面编码（Bitplane Coding）**：FGS的核心在于比特平面编码技术的应用。比特平面编码是一种逐层编码的方法，可以将视频信号分解成多个比特平面，每个比特平面代表一个特定的质量级别。这种方式使得接收端可以根据当前网络状况选择解码部分或全部比特平面，从而实现不同质量级别的视频播放。 2. **适应性传输**：在网络视频传输中，尤其是在互联网环境中，网络带宽的变化非常大。传统的视频编码技术往往假设编码器了解传输通道的容量，并据此优化视频质量。而在基于互联网的流媒体视频传输中，由于存在视频服务器以及变化的网络条件，编码器不再能够准确预测通道容量。因此，FGS技术通过比特平面编码，使得视频可以在任何可用的网络条件下尽可能高效地传输。 3. **组合使用**：除了单独使用FGS外，还可以与时间可伸缩性结合使用，即Temporal and Fine Granularity Scalability (T&FGS)。时间可伸缩性允许视频根据帧率的不同进行分级，而FGS则侧重于图像质量的不同层次。二者的结合使用可以极大地提高视频传输的灵活性和效率。 4. **计算资源分配**：随着软件视频客户端/解码器的应用越来越广泛，它们需要与其他应用程序共享计算资源。在这种情况下，FGS技术通过提供不同质量级别的视频，可以有效地减轻解码器的计算负担，确保视频的流畅播放。 #### 总结细粒度可伸缩性（FGS）作为MPEG-4标准中的一个重要组成部分，为互联网视频传输提供了一种强大的解决方案。通过比特平面编码等关键技术的应用，FGS不仅提高了视频传输的灵活性和效率，还解决了网络带宽变化带来的问题。未来，随着网络技术和视频编码技术的不断发展，FGS技术的应用场景将更加广泛，为用户提供更高质量的视频体验。

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 3, MARCH 2001 301

Overview of Fine Granularity Scalability in

MPEG-4 Video Standard

Weiping Li, Fellow, IEEE

Abstract—Streaming Video Profile is the subject of an Amend-

ment of MPEG-4, and is developed in response to the growing need

on a video-coding standard for streaming video over the Internet.

It provides the capability to distribute single-layered frame-based

video over a wide range of bit rates with high coding efficiency. It

also provides fine granularity scalability (FGS), and its combina-

tion with temporal scalability, to address a variety of challenging

problems in deliveringvideo over the Internet. This paper provides

an overview of the FGS video coding technique in this Amendment

of the MPEG-4.

Index Terms—Bitplane coding, Internet, media, MPEG, scala-

bility, standards, streaming, video.

I. INTRODUCTION

HE OBJECTIVE of video coding has traditionally been

to optimize video quality at a given bit rate. Due to the

network video applications, such as Internet streaming video,

the objective is somewhat changed. This change is necessary

because network video has introduced a new system configura-

tion, as illustrated in Fig. 1, and the network channel capacity

varies over a wide range depending on the type of connections

and the network traffic at any given time.

In a traditional communication system, the encoder com-

presses the input video signal into a bit rate that is less than,

and close to, the channel capacity, and the decoder reconstructs

the video signal using all the bits received from the channel.

In such a model, two basic assumptions are made. The first

is that the encoder knows the channel capacity. The second is

that the decoder is able to decode all the bits received from

the channel fast enough to reconstruct the video. These two

basic assumptions are challenged in Internet streaming video

applications. First of all, due to the video server used between

the encoder and the channel, as shown in Fig. 1, plus the

varying channel capacity, the encoder no longer knows the

channel capacity and does not know at which bit rate the video

quality should be optimized. Secondly, more and more appli-

cations use a software video client/decoder that has to share

the computational resources with other operations on the user

terminal. The video decoder may not be able to decode all the

bits received from the channel fast enough for reconstruction

of the video signal. Therefore, the objective of video coding

for Internet streaming video is changed to optimizing the video

quality over a given bit rate range instead of at a given bit

Manuscript received June 15, 2000; revised December 7, 2000. This paper

was recommended by Guest Editors M. R. Civanlar, A. Luthra, S. Wenger, and

W. Zhu.

The author is with WebCast Technologies, Inc., Mountain View, CA 94041

USA (e-mail: li@webcasttech.com).

Publisher Item Identifier S 1051-8215(01)02236-4.

Fig. 1. System configuration for Internet Streaming Video.

Fig. 2. Illustration of video coding performance.

rate. The bitstream should be partially decodable at any bit rate

within the bit rate range to reconstruct a video signal with the

optimized quality at that bit rate. Fig. 2 illustrates this point.

The horizontal axis in Fig. 2 indicates the channel bit rate,

while the vertical axis indicates the video quality received by

a user. The distortion-rate curve indicates the upper bound in

quality for any coding technique at any given bit rate. The three

staircase curves indicate the performance of an optimal nonscal-

able coding technique. Once a given bit rate is chosen—either

low, medium, or high—the nonscalable coding technique tries to

achieve the optimal quality indicated by having the upper corner

of the staircase curve very close to the distortion-rate curve. If

the channel bit rate happens to be at the video-coding bit rate,

the received video quality is the best. However, if the channel

bit rate is lower than the video coding bit rate, a so-called “dig-

ital cutoff” phenomenon happens and the received video quality

becomes very poor. On the other hand, if the channel bit rate is

higher than the video-coding bit rate, the received video quality

does not become any better. Scalable video coding has been

an interesting topic. In MPEG-2 and MPEG-4, several layered

302 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 11, NO. 3, MARCH 2001

Fig. 3. SNR scalability decoder defined in MPEG-2.

scalability techniques, namely, SNR scalability, temporal scala-

bility, and spatial scalability, have been included. In such a lay-

ered scalable coding technique, a video sequence is coded into

a base layer and an enhancement layer. The enhancement layer

bitstream is similar to the base layer bitstream in the sense that it

has to be either completely received and decoded or it does not

enhance the video quality at all. As shown in Fig. 2, such lay-

ered scalability techniques change the nonscalable single stair-

case curve to a curve with two stairs. The base-layer bit rate

determines where the first stair is and the enhancement layer

bit rate determines the second stair. The two curves shown in

Fig. 2 for layered scalability have different characteristics. The

first one has a poor performance for the base layer and a good

performance for the enhanced video. The second is the oppo-

site, namely, poor performance for the enhancement layer and

good performance for the base layer. More detailed discussions

on some specific layered scalable coding techniques are pre-

sented in the next section. As shown in Fig. 2, the desired objec-

tive of video coding for Internet streaming video is to achieve

the continuous curve that parallels the distortion-rate curve with

a single bitstream. This is the objective of the fine granularity

scalability (FGS) video-coding technique in the Amendment of

MPEG-4 [1].

This paper provides an overview on the FGS video coding

technique defined in MPEG-4. To fully take advantage of the

FGS video-coding technique for streaming video applications,

many network-related issues have to be dealt with, which are

not addressed in this paper. The next section briefly reviews

several layered scalable coding techniques from which one

can see the similarities and differences between the FGS

video coding technique and the other layered scalable video

techniques. In Section III, bit-plane coding of the DCT coeffi-

cients is described and compared with conventional run-level

coding of the discrete cosine transform (DCT) coefficients,

showing that bit-plane coding is more efficient than run-level

coding. In Section IV, the basic FGS coding tools based on

bit-plane coding are described. In Section V, the performance

of the FGS coding technique is compared with multilevel

SNR scalability, nonscalable coding, and simulcast in terms of

coding efficiency. In Section VI, several advanced features of

FGS coding are described. Finally, the paper is completed with

some conclusions in Section VII and an Acknowledgment in

Section VIII.

II. B

RIEF REVIEW OF LAYERED SCALABLE CODING

TECHNIQUES

Before presenting the FGS technique in MPEG-4, other

scalable video coding techniques (SNR, temporal, spatial)

are briefly reviewed in this section. More detailed descrip-

tions can be found in [2]–[22]. Signal-to-noise ratio (SNR)

scalability is a technique to code a video sequence into two

layers at the same frame rate and the same spatial resolution,

but different quantization accuracy. Fig. 3 shows the SNR

scalability decoder defined in MPEG-2 video-coding standard

[2]–[4]. The base-layer bitstream is decoded by the base layer

variable-length decoder (VLD) first. The inverse quantizer in

the base layer produces the reconstructed DCT coefficients.

The enhancement bitstream is decoded by the VLD in the

enhancement layer and the enhancement residues of the DCT

coefficients are produced by the inverse quantizer in the

enhancement layer. A higher accuracy DCT coefficient is ob-

tained by adding the base-layer reconstructed DCT coefficient

and the enhancement-layer DCT residue. The DCT coefficients

with a higher accuracy are given to the inverse DCT (IDCT)

unit to produce reconstructed image domain residues that are to

be added to the motion-compensated block from the previous

frame.

In this SNR scalability decoder, the enhancement-layer infor-

mation is used in the motion-prediction loop. Therefore, there

are the following four possible results depending on the cor-

responding SNR scalability encoder and whether the enhance-

ment layer information is received by the decoder or not.

1) If the encoder uses the enhancement-layer information

in the motion-prediction loop and the enhancement-layer

information is received by the decoder, the enhancement-

layer coding efficiency is high.

2) If the encoder uses the enhancement-layer information

in the motion-prediction loop and the enhancement-layer

LI: OVERVIEW OF FGS IN MPEG-4 VIDEO STANDARD 303

Fig. 4. A temporal scalability structure.

Fig. 5. A single-loop spatial scalability decoder.

information is not received by the decoder, drift happens

in the base layer and coding efficiency is low.

3) If the encoder does not use the enhancement-layer infor-

mation in the motion-prediction loop and the enhance-

ment-layer information is received by the decoder, drift

happens in the enhancement layer and coding efficiency

is low.

4) If the encoder does not use the enhancement-layer infor-

mation in the motion-prediction loop and the enhance-

ment-layer information is not received by the decoder, the

result is the same as using the base layer only.

Therefore, the choice is between the two curves illustrated in

Fig. 2 for the layered scalable coding techniques, namely, either

the base layer has a poor performance to ensure a good perfor-

mance for the enhancement layer, or the enhancement layer has

a poor performance to ensure a good performance for the base

layer.

Temporal scalability is a technique to code a video sequence

into two layers at the same spatial resolution, but different frame

rates. The base layer is coded at a lowerframe rate. The enhance-

ment layer provides the missing frames to form a video with a

higher frame rate. Coding efficiency of temporal scalability is

high and very close to nonscalable coding. Fig. 4 shows a struc-

ture of temporal scalability. Only

-type prediction is used in

the base layer. The enhancement-layer prediction can be either

-type or -type from the base layer or -type from the en-

hancement layer.

Spatial scalability is a technique to code a video sequence

into two layers at the same frame rate, but different spatial reso-

lutions. The base layer is coded at a lower spatial resolution. The

reconstructed base-layer picture is up-sampled to form the pre-

diction for the high-resolution picture in the enhancement layer.

Fig. 5 shows a single-loop spatial scalability decoder. The ad-

vantage of single-loop spatial scalability is its simplicity. If the

spatial resolution of the base layer is the same as that of the en-

hancement layer, i.e., the up-sampling factor being 1, this spatial

scalability decoder can be considered as an SNR scalability de-

coder too. Unlike the SNR scalability decoder in MPEG-2, the

above spatial scalability decoder does not include the enhance-

ment-layer information into the prediction loop. Therefore, if

the corresponding encoder does not include the enhancement-

layer information into the prediction loop either, the base-layer

drift does not exist. Coding efficiency of the enhanced video

using such an “open-loop” scalable coding method suffers from

the fact that the enhancement information of the previous frame

is not used in the prediction for the current frame.

The spatial scalability decoders defined in MPEG-2 and

MPEG-4 use two prediction loops, one in the base layer and the

other in the enhancement layer [2]–[6]. The MPEG-2 spatial

scalable decoder uses as prediction a weighted combination of

up-sampled reconstructed frame from the base layer and the

previously reconstructed frame in the enhancement layer, while

the MPEG-4 spatial scalable decoder allows a “bi-directional”

prediction using up-sampled reconstructed frame from the

base layer as the “backward reference” and the previously

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