A HYBRID TRANSFORMATION TECHNIQUE FOR ADVANCED

VIDEO CODING

M. Ezhilarasan, P. Thambidurai
Department of Computer Science & Engineering and Information Technology,
Pondicherry Engineering College, Pondicherry – 605 014, India
mrezhil@yahoo.com

ABSTRACT
A Video encoder performs video data compression by having combination of three
main modules such as Motion estimation and compensation, Transformation, and
Entropy encoding. Among these three modules, transformation is the module of
removing the spatial redundancy that exists in the spatial domain of video
sequence. Discrete Cosine Transformation (DCT) is the defacto transformation
method in existing image and video coding standards. Even though the DCT has
very good energy preserving and decorrelation properties, it suffers from blocking
artifacts. To overcome this problem, a hybridization method has been incorporated
in transformation module of video encoder. This paper presents an hybridization in
the transformation module by incorporating DCT as transformation technique for
inter frames and a combination of wavelet filters for intra frames of video
sequence. This proposal is also applied in the existing H.264/AVC standard.
Extensive experiments have been conducted with various standard CIF and QCIF
video sequences. The results show that the proposed hybrid transformation
technique outperforms the existing technique used in the H.264/AVC considerably.

Keywords: Data Compression, DCT, DWT, Video Coding, Transformation.

1 INTRODUCTION In Advanced Video Coding (AVC) [6], video is

captured as a sequence of frames. Each frame is
Transform coding techniques have become the compressed by partitioning it as one or more slices,
important paradigm in image and video coding where each slice consists of sequence of macro
standards, in which the Discrete Cosine Transform blocks. These macro blocks are transformed,
(DCT) [1][2] is applied due to its high decorrelation quantized and encoded. The transformation module
and energy compaction properties. In the past two converts the frame data from time domain to
decades, more contributions focused on Discrete frequency domain, which intends to decorrelate the
Wavelet Transform (DWT) [3][4] for its energy (i.e., amount of information present in the
performance in image coding. The two most popular frame) present in the spatial domain. It also converts
techniques such as DCT and DWT are well applied the energy components of the frame into small
on image and video coding applications. numbers of transform coefficients, which are more
International Organization for Standards / efficient for encoding rather than their original
International Electro technical Commission frame. Since the transformation module is reversible
(ISO/IEC) and International Telecommunications in nature, this process does not change the
Union – Telecommunication standardization sector information content present in the source input signal
(ITU-T) organizations have developed their own during encoding and decoding process. By
video coding standards viz., Moving Picture Experts information theory, transformed coefficients are
Group MPEG-1, MPEG-2, MPEG-4 for multimedia reversible in nature.
and H.261, H.263, H.263+, H.263++, H.26L for As per Human Visual System (HVS), human
videoconferencing applications. Recently, the MPEG eyes are highly sensitive on low frequency signals
and the Video Coding Experts Group (VCEG) have than the high frequency signals. The decisive
jointly designed a new standard namely, H.264 / objective is this paper is to develop a hybrid
MPEG-4 (Part-10) [5] for providing better technique that achieves higher performance in the
compression of video sequence. There has been a parameters specified above than the existing
tremendous contribution by researchers, experts of technique used in the current advanced video coding
various institutions and research laboratories for the standard.
past two decades to take up the recent technology In this paper, a combination of orthogonal and
requirements in the video coding standards. bi-orthogonal wavelet filters have been applied at

Ubiquitous Computing and Communication Journal 1

different decomposition levels for intra frames and 2.1 Basics of Transformation
DCT for inter frames of video encoder. Even though
intra frames are coded with wavelet transform, the From the basic concepts of information theory,
impact of this can be seen in inter frame coding. coding of symbols in vectors is more efficient than
With better quality anchor pictures are retained in coding of symbols in scalars [8]. By using this
frame memory for prediction, the remaining inter phenomenon, a group of blocks of consecutive
frame pictures are more efficiently coded with DCT. symbols from the source video input are taken as
The proposed transformation method is also vectors. There is high correlation in the neighboring
implemented on H.264/AVC reference software [7]. pixels in an image or intra-frame of video.
The paper is organized as follows. In Section 2, the Transformation is a reversible model [9] by theory,
basics of the transform coding methods are which decorrelates the symbols in the given blocks.
highlighted. The proposed hybrid transformation In the recent image and video coding standards the
technique has been described in section 3. Extensive following transformation techniques are applied due
experimental results and discussion have been given to their orthonormal property and energy
in section 4 followed by conclusion in section 5. compactness.

2 BASICS OF TRANSFORM CODING 2.1.1 Discrete Cosine Transform

The Discrete Cosine Transform, a widely used
For any inter-frame video coding standards, the transform coding technique in image and video
basic functional modules are motion estimation and compression algorithms. It is able to perform de-
compensation, transformation, quantization and correlation of the input signal in a data-independent
entropy encoder. As shown in the Fig. 1, the manner. When an image or a frame is transformed by
temporal redundancies exists in successive frames DCT, it is first divided into blocks, typically of size
are minimized or reduced by motion estimation and of 8x8 pixels. These pixels are transformed
compensation module. The residue or the difference separately without any influence from the other
between the original and motion compensated frame surrounding blocks. The top left coefficient in each
is applied into the sequence of transformation and block is called the DC coefficient, and is the average
quantization modules. The spatial redundancy exists value of the block. The right most coefficients in the
in neighboring pixels in the image or intra-frame is block are the ones with highest horizontal frequency,
minimized by these modules. while the coefficients at the bottom have the highest
vertical frequency. This implies that the coefficient
in the bottom right corner has the highest frequencies
of all the coefficients.
The forward DCT of a discrete signal for
original image f(i,j) for (MxN) block size and inverse
DCT (IDCT) of reconstructed image f% (i, j) for the
same (MxN) block size are defined as

2C(u)C(v) M −1 N −1 (2i + 1)uπ (2 j + 1)vπ (1)

F(u,v) = ∑ ∑ cos 2M cos 2 N f (i , j )
MN i =0 j = 0

M −1 N −1
2C(u)C(v) (2i + 1)uπ (2 j + 1)vπ (2)
%
f(i,j) = ∑∑ cos cos F (u , v )
u = 0 v =0 MN 2M 2N

Where i, u = 0,1,…,M-1, j, v = 0,…,N-1 and the

Figure 1: Basic Video encoding module.
constants C(u) and C(v) are obtained by
2
The transformation module converts the residue C ( x) = if x = 0
2
symbols from time domain into frequency domain,
which intends decorrelate the energy present in the =1 otherwise
spatial domain. This is so appropriate for
quantization. Quantized transform coefficients and MPEG standards apply DCT for video
motion displacement vectors obtained from motion compression. The compression exploits spatial and
estimation and compensation module are applied into temporal redundancies which occur in video objects
entropy encoding (Variable Length Coding) module, or frames. Spatial redundancy can be utilized by
where it removes the statistical redundancy. These simply coding each frame separately. This technique
modules are briefly introduced as follows. is referred to as intra frame coding. Additional
compression can be achieved by taking advantage of
the fact that consecutive frames are often almost
identical. This temporal compression has the

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potential for a major reduction over simply encoding φ (t ) = ∑ 2h0 [n]φ (2t − n) (3)
each frame separately, but the effect is lessened by n∈Z
the fact that video contains frequent scene changes. The dilation function is recipes for finding a function
This technique is referred to as inter-frame coding. that can be build from a sum of copies of itself that
The DCT and motion compensated Inter-frame are scaled, translated, and dilated. Equation (3)
prediction are combined. The coder subtracts the expresses a condition that a function must satisfy to
motion-compensated prediction from the source be a scaling function and at the same time forms a
picture to form a ‘prediction error’ picture. The definition of the scaling vector h0. The wavelet at the
prediction error is transformed with the DCT, the coarser level is also expressed as
coefficients are quantized using scalar quantization ψ (t ) = ∑ 2h1 [ n]φ (2t − n) (4)
and these quantized values are coded using an n∈Z
arithmetic coding. The coded luminance and The discrete high-pass impulse response h1[n],
chrominance prediction error is combined with ‘side describing the details using the wavelet function, can
information’ required by the decoder, such as motion be derived from the discrete low-pass impulse
vectors and synchronizing information, and formed response h0[n] using the following equation.
into a bit stream for transmission. This technique h1 [n] = (−1) n h0 [1 − n] (5)
works well with a stationary background and a
The number of coefficients in the impulse
moving foreground since only the movement in the
coefficients in the impulse response is called the
foreground is coded.
Despite all the advantages of JPEG and MPEG number of taps in the filter. For orthogonal filters,
compression schemes based on DCT namely the forward transform and its inverse are transposing
simplicity, satisfactory performance, and availability of each other, and the analysis filters are identical to
the synthesis filters.
of special purpose hardware for implementation;
these are not without their shortcomings. Since the
input image needs to be “blocked,” correlation across 2.2 Quantization
A Quantizer [10][11] simply reduces the number
the block boundaries is not eliminated. The result is
noticeable and annoying “blocking artifacts” of bits needed to store the transformed coefficients
particularly at low bit rates. by reducing the precision of those values. Since this
is a many to one mapping, it is a lossy process and is
the main source of compression in an encoder.
2.1.2 Discrete Wavelet Transform
Quantization can be performed on each individual
Wavelets are functions defined over a finite
interval and having an average value of zero. The coefficient, which is referred as scalar quantization.
basic idea of the wavelet transform is to represent Quantization can also be performed on a group of
any arbitrary function as a superposition of a set of coefficients together, and which is referred as vector
quantization.
such wavelets or basis functions. These basis
functions or child wavelets are obtained from a Uniform quantization is a process of partitioning
single prototype wavelet called the mother wavelet, the domain of input values into equally spaced
by dilations or scaling and translations. Wavelets are intervals, except outer intervals. The end points of
used to characterize detail information. The partition intervals are called the quantizer decision
averaging information is formally determined by a boundaries. The output or reconstruction value
kind of dual to the mother wavelet, called the scaling corresponding to each interval is taken to be the
midpoint of the interval. The length of each interval
function φ (t). The main concept of wavelets is that is referred to as the step size (fixed in the case of
at a particular level of resolution j, the set of uniform quantization), denoted by the symbol ∆.
translates indexed by n form a basis at that level. The step size ∆ is given by
The set of translates forming the basis at the j+1 next 2X max
∆= (6)
level, a coarser level, can all be written as a sum of M
weights times the level-j basis. The scaling function Where M = number of level of quantizer, Xmax is the
is chosen such that the coefficients of its translation maximum range of input symbols.
are all necessarily bounded. In this work, a quantizer used in H.264 has been
The scaling function, along with its translation, considered for inter-frame motion compensated
forms a basis at the coarser level j+1 but not level j. predictive coding, which allows acceptable loss in
Instead, at level j the set of translates of the scaling quality for the given video sequences.
function φ (t) along with the set of translates of the
mother wavelet φ (t) do form a basis. Since the set of 2.3 Motion Estimation
Motion estimation (ME) [12] is a process to
translates of the scaling function φ (t) at a coarser estimate the pixels of the current frame from
level can be written exactly as a weighted sum of reference frame(s). Block matching motion
translates at a finer level, the scaling function must estimation or block matching algorithm (BMA),
satisfy the dilation function which is temporal redundancy removal technique

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between two or more successive frames, is an coding is more complex than Huffman coding on its
integral part for most of the motion compensated implementation. CAVLC used in H.264 has been
video coding standards. Frames are being divided considered in the experiments for entropy encoding
into regular sized blocks, so referred as macro blocks process.
(MB). Block-matching method is to find the best-
matched block from the previous frame. Based on a 2.5 Motivation for this work
block distortion measure (BDM), the displacement of DCT is best transformation technique for Motion
the best-matched block will be described as the estimation and compensated predictive coding
motion vector (MV) to the block in the current frame. models. Due to blocking artifacts problems
The best match is usually evaluated by a cost encountered in DCT, sub band coding methods are
function based on a BDM such as Mean absolute considered as an alternative for this problem. DWT
difference (MAD) defined as is the best alternative method because of its energy
1 M −1 N −1 | c( x + k , y + l ) − p( x + k + i, y + l + compaction and preservation property. Due to
MN ∑ ∑
MAD(i, j) = j) |
k =0 l =0
(7) ringing artifacts incurred in DWT, there is a
where M x N is the size of the macro block, c(.,.) and tremendous contribution from the researchers,
p(.,.) denote the pixel intensity in the current frame experts from various institutes and research labs for
and previously processed frames respectively, (k,l) is past two decades.
the coordinates of the upper left corner of the current In addition to the transformation module, In
block, and (x,y) represents the displacement in pixel DCT-based Motion compensated Predictive
which is relative to the position of current block. (MCP) [15] coding architecture, previously
After checking each location in the search area, the processed frames are considered as reference frames
motion vector is then determined as the (x,y) at to predict the future frames. Even though the
which the MAD has the minimum value. In this wok, transformation module is energy preserving and
an exhaustive full search has been applied for motion lossless module, it is irreversible in experiments.
compensated prediction technique. Subsequently the transformed coefficients are
quantized to achieve higher compression leads
2.4 Entropy Encoding further loss in the frame, which are to be considered
Based on scientist Claude E. Shannon [8], the as reference frames stored in frame memory for
entropy η of an information source with alphabet S = future frame prediction. Decoded frames are used for
{s1, s2, …, s3} is defined as the prediction of new frames as per the MCP coding
n
1 technique. JPEG 2000 [16] proved that high quality
η = H ( S ) = ∑ pi log 2
i =1 pi image compression can be achieved by applying
(8)
DWT. This motivates us to have a combination of
Where pi is the probability of symbol si in S. The
orthogonal and bi-orthogonal wavelet filters at
term log2 1 indicates the amount of information different level of decompositions for intra frames and
pi

contained in si, which corresponds to the number of DCT for inter frames of video sequence.
bits needed to encode si. An entropy encoder further
compresses the quantized values to give better 3 PROPOSED HYBRID TRANSFORMATION
compression ratio. It uses a model to accurately WITH DIFFERENT COMBINATION OF
determine the probabilities for each quantized value WAVELET FILTERS
and produces an appropriate code based on these
probabilities so that the resultant output code stream In order to improve the efficiency of
will be smaller than the input stream. The most transformation phase, the following techniques are
commonly used entropy encoders are the Huffman adopted in the transformation module of the
encoder [13] and the arithmetic encoder [14]. It is CODEC. Orthogonal wavelet filters such as Haar
important to note that a properly designed entropy filter and Daubechies 9/7 filters are considered for
encoder is absolutely necessary along with optimal intra frames and Discrete Cosine Transform for inter
signal transformation to get the best possible frames of video sequence. Figure 2 illustrates an
compression. overview of the encoder of H.264/AVC with a
Arithmetic coding is a more modern coding hybrid transformation technique. Previously
method that usually outperforms Huffman coding in processed frames (F’n-1) are used to perform Motion
practice. In arithmetic coding, a message is Estimation and Motion Compensated Prediction,
represented by an interval of real numbers between 0 which yields motion vectors.
and 1. As the message becomes longer, the interval These motion vectors are used to make a motion
needed to represent it becomes smaller, and the compensated frame. In the case of inter frames, the
number of bits needed to specify that interval grows. frame is subtracted from the current frame (Fn) and
Successive symbols of the message reduce the size of the residual frame is transformed using Discrete
the interval in accordance with the symbol Cosine Transform (T) and quantized (Q). In the case
probabilities generated by the model. The arithmetic of intra frame, the current frame is transformed using

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Discrete Wavelet Transform (DWT) with different avoidance of undesirable blocking artifacts, the intra
orthogonal wavelet filters such as Haar and frame is reconstructed with high quality. The first
Daubechies and quantized (Q). The quantized frame in a GOF is intra frame coded. Frequent intra
transform coefficients are then entropy coded and frames enable random access to the coded stream.
transmitted or stored through NAL along with the Inter frames are predicted from previously decoded
motion vectors found in the motion estimation intra frames.
process.
+ X
4 EXPERIMENTAL RESULTS AND
Fn T Q Reorder Entropy
encoder DISCUSSION
ME

Inter DWT
F’n-1 MC NAL The experiments were conducted for three CIF
Choose Intra Intra
video sequences such as “Bus” (352x288, 149
intra
prediction
prediction
IDWT
frames), “Stefan” (352x288, 89 frames) and “Flower
+
+
Garden” (352x288, 299 frames), and two QCIF
F’n Filter IT Q’1
video sequences like “Suzie” (176x144, 149 frames)
and “Mobile” (176x144, 299 frames). The
Figure 2: Encoder in the hybrid transformation with
experimental results show that the developed hybrid
wavelet filters.
transform coding with wavelet filters combination
outperforms over conventional DCT based video
For predicting the subsequent frames from the
coding in terms of quality performance.
anchor intra frames, the quantized transform
Peak Signal to Noise Ratio (PSNR) is commonly
coefficients are again dequantized (Q’1) followed by
used to measure the quality. It is obtained from
inversely transformed (IT) and retained in the frame
logarithmic scale and it is Mean Squared Error
store or store memory for motion compensated
(MSE) between the original and reconstructed image
prediction.
or video frame with respect to the highest available
symbol in the spatial domain.
Table 1: Biorthogonal wavelets filter coefficients.
(2 n − 1) 2 (9)
Analysis Filter Coefficients P S N R = 1 0 log 1 0 dB
M SE
i Lowpass Filter g L(i) Highpass Filter g H(i)
0 0.602949018236359 1.115087052456994 where n is the number of bits per image symbol.
±1 0.266864118442872 -
±2 - - The fundamental tradeoff is between bit rate and
fidelity [17]. The ability of any source encoding
±3 - 0.091271763114249 system is to make this tradeoff as acceptable by
±4 0.026748757410809 - keeping moderate coding efficiency.
Synthesis Filter Coefficients
i Lowpass Filter h L(i) Highpass Filter h H(i) Table 2: Proposed combination of wavelets filters.
0 1.115087052456994 0.602949018236379 Proposed 1st level 2nd level
±1 0.591271763114247 - combination Decomposition Decomposition
±2 - - P1 Haar Haar
P2 Haar Daub
±3 - 0.016864118442874 P3 Daub Haar
±4 - 0.026748757410809 P4 Daub Daub

In the case of intra frames, inverse Discrete Table 2 shows the combination of orthogonal
Wavelet Transform is applied in order to obtain Haar and Daubechies 9/7 wavelet filters in different
reconstructed reference frames (F’n) through de- level of decompositions in transform coding. These
blocking filter for inter frames of video sequence. combinations are simulated in H.264/AVC codec,
The hybrid transformation technique employs where the DCT is the de-facto transformation
different techniques for different categories of frames. technique for both intra frame and inter frames of
Intra frames are coded using both Haar wavelet filter video sequence processing.
coefficients [0.707, 0.707] and bi-orthogonal Table 3 shows the performance comparison of
Daubechies 9/7 wavelet filter coefficients as shown the quality parameter in terms of Peak Signal-to-
in Table 1 [16] in different combinations on different Noise Ratio (PSNR) for the existing de-facto DCT
decomposition levels. Because of wavelet’s transformation with combination of proposed
advantages over DCT such as complete spatial wavelet filters. The values in the table represent the
correlation among pixels in the whole frame, average PSNR improvement for Luminance (Y)

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component and Chrominance (U and V) components. considered in this paper includes the PSNR
As per Human Vision System, human eyes are performance. The performance evaluations show that
highly sensitive on Luminance than the Chrominance the hybrid transformation technique outperforms the
components. In this analysis, both Luminance and existing DCT transformation method used in
Chrominance components are considered due to the H.264/AVC significantly. The experimental results
importance of colour in near lossless applications. also demonstrate that the combination of Haar
There is 0.12 dB Y-PSNR improvement in P4 wavelet filter in 1st level of decomposition and
combination with DCT transformation for ‘Bus’ CIF Daubechies wavelet filters in 2nd level of
sequence. When the comparison has been made for decomposition outperforms other combination and
‘Stefan’ CIF sequence, 0.31 dB Y-PSNR the original DCT used in the existing AVC standard.
improvement has been achieved in P1 combination
with existing transformation. 0.14 dB Y-PSNR ACKNOWLEDGEMENT
quality has been obtained with DCT transformation The authors wish to thank S. Anusha, A. R.
in P4 combination for ‘Flower-Garden’ CIF Srividhya, S. Vanitha, V. Rajalakshmi, R. Ramya, M.
sequence. Vishnupriya A. Arun, V. Vijay Anand, S. Dhinesh
Kumar and P. Navaneetha Krishnan undergraduate
Table 3: PSNR comparison for the various video students for their valuable help.
sequences.
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