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IMAGE AND VIDEO DENOISING USING ADAPTIVE

FILTER Tejal Patel

1, Zaid M. Shaikhji

2

Student, Electronics and Comm. Dept., SNPITRC, Bardoli, Surat, Gujarat, India 1

Assistant Professor, Electronics and Comm. Dept., SNPITRC, Bardoli, Surat, Gujarat, India 2

Abstract: The demand for communication with moving video picture is increasing rapidly.

Video compression is the wide spread field in communication engineering and computer

science that deals with illustration of video data, for storage and/or transmission. Block

based motion estimation and motion compensation technique is widely used in most of

video coding standards including H.264/AVC (Advance Video Coding) to compress raw

video. Motion compensated block is transform coded and quantize before entropy coding to

achieve high coding efficiency. But all block base operations introduces annoying blocking

artifacts and degrade the quality of reconstructed video. In order to improve the quality of

the reconstructed video, several de-blocking algorithms have been proposed. Post filtering

based offset-shift algorithm and novel algorithm are one of them. In this report offset-shift

algorithm is applied to motion compensated frames and method of computing adaptive

threshold T based on movement associated with moving sequence is proposed. This report

also describes novel algorithm with modified in filtering procedure. Experimental results

indicate that offset-shift algorithm outperforms with motion adaptive threshold in

comparison of fixed threshold for highly compressed standard test images and video

sequences. Experimental results also show that offset and shift algorithm provides better

improvement in PSNR (dB) and SSIM compared to novel algorithm.

Keywords: H.264/AVC deblocking filter, low bit-rate mobile video, offset-and-shift

technique, blocking artifact, block-based coding.

I. INTRODUCTION

Video compression is important topics in today's research in the field of multimedia.

Without video compression it difficult to transmit videos over a network or to store

them on nowadays supports, due to large amount of information that they contain. The

amount of information contained in a raw video can be calculated by considering its

height (H), width (W), number of channels (usually three), colour depth (usually

minimum 8 bits) and sequence length (expressed in number of frames) as shown in

equation (1.1).

Data rate=Numbers of Frames x W x H x Channels of Numbers Depth x Color (1)

Data rate required for 30-fps (Frames per Second) full HD (High Definition) colour

video is 301920 1088 3 8 = 1.5 Gbps, which is impractical for today’s communication or

storage infrastructure [1]. Such high data rate indicates the requirement of video

encoding standard and hence several video coding standards have been developed for

compression of raw video. Most of the video coding standards (including H.264) use

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blocks Motion Estimation (ME) and Motion Compensation (MC) technique for raw

video compression. Here all MC blocks are further transform coded and quantized for

highly compression. But quantization of the transform coefficients and motion

compensation of block can cause visually disturbing discontinuities at the block

boundaries and reduce visual quality of decoded videos. Here discontinuities at the

block boundaries are known as Blocking Artifacts [2]. Here de-blocking filter is

necessary to remove these blocking artifacts produced during decoding process.

De-blocking filter helps in improving the subjective and objective quality of the output

frames, but it is generally computationally intensive. De-blocking filter can easily

account for one-third of the computational complexity of a decoder. This complexity is

mainly based on the high adaptivity of the filter, which requires conditional processing

on the block edge [3]. Hence main problem is to implement less complex de-blocking

algorithm for decoder which remove blocking artifacts form frames and increase video

quality. The rest of the paper is organized as follows: Section II Review the basic

concepts related to video coding & H.264 standard. Same chapter also describes the

literature review. Section III presents novel de-blocking algorithm, offset-shift de-

blocking algorithm, H.264/AVC de-blocking algorithm Directional de-blocking

algorithm and adaptive de-blocking algorithm for mobile video. Experimental results

compared with de-blocking algorithms and conclusion is described in Section IV and

Section V respectively.

II. BACKGROUND OF H.264 AND LITERATURE REVIEW

The basic idea about video coding or compression is necessary before discussion of any video

coding standard. This chapter describes the basic concept of video compression and overview of

H.264 video compression standard.

A. Basic of Video Compression

Any video can be considered as a sequence of frames. Also in any video two consecutive

frames are quite similar which means that large amount of redundancies are present between

that two frames. Hence generally video compression is performed by exploiting the inherent

redundancies present in the video frames.

B. H.264 Standard

For video compression numbers of encoding standards have been proposed. H.264/AVC

(Advance Video Coding) [4] is one of them which employ block based motion estimation

(ME), motion compensation (MC), integer transform and quantization to compress raw video,

which is developed by the ITU-T Video Coding Experts Group (VCEG) together with the

International Organization for Standardization (ISO)/International Electro-technical

Commission (IEC) joint working group, the Moving Picture Experts Group (MPEG) [4].

Figure.1. Block diagram of H.264 encoder [5][17]

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A major goal while designing the H.264 (or MPEG-4 AVC) codec was to create a standard

capable of providing good video quality at substantially lower bit rates than previous

standards. Figure 2.2 shows the basic block diagram of H.264 encoder.

C. Literature Review

There are two main approaches to integrate de-blocking filters into video codec. De-blocking

filters can be used either as post filters or loop filters [3]. H.264/AVC de-blocking filter [3] is

in-loop filter which apply 1-D weak filter or strong filter on block boundary using threshold.

Post filter only operate on the display buffer outside of the coding loop, so the post-

processing requires no modifications of existing standards to get better quality and it is the

most practical solution to remove the blocking artifacts. In other words post-filters offer

maximum freedom for decoder implementations. Whereas loop filters operate within the

coding loop. That is, the filtered frames are used as reference frames for encoding of

subsequent frames. This forces all standard decoders to perform identical filtering in order to

stay in synchronization with the encoder and increases the computational complexity [3], [8],

[9]. According to [8] and [10], post filtering based de-blocking algorithm is better compared

to in-loop filtering based algorithm due to less computational complexity.

Y. L. Lee et al. [11] classify the blocking artifacts in BDCT-coded images into three types as

follows: grid noises in the smooth area, staircase noises along the image edges, and corner

outliers in the cross points of the four 8 x 8 DCT blocks. Numbers of post filtering based de-

blocking algorithm have been proposed to remove above all three kinds of noises. De-

blocking filter in [9] first remove high frequency components from frame and then according

to block activity it uses smooth filtering mode, non-symmetric filtering mode, boundary

adjustment mode and corner outliers removal mode to remove blocking artifacts. Lee et al.’s

method [11] applied a 2-D filter to the edge map obtained by sobel operator. However, Lee et

al.’s method exhibits poor de-blocking performance in edge or texture areas, since Sobel

operator often detects blocking artifacts as real edges. The filters [12] is applied only to the

boundary pixels, which are two pixels straddling the block boundary, and thus do not remove

the grid noises. Offset-shift algorithm in [8] is able to remove all three kinds of noises but it

uses non adaptive fix threshold T to remove noises. Due to fix threshold this algorithm gives

poor improvement in PSNR for other images. Another novel algorithm proposed in [10] and

is not able to remove above all three kinds of noises as they apply 1-D filter or 2-D filter only

in middle boundary pixels of two adjacent 8x8 block of frame for horizontal filtering and

vertical filtering. Next chapter describes concepts and working of de-blocking filter.

III. DEBLOCKING FILTERS

This paper describes de-blocking filter algorithms as post filter to improve the visual quality

of frame. Four algorithms are described, Novel algorithm in section III.A and offset and shift

techniques in section III.B, H.264/AVC de-blocking algorithm in section III.C, Directional

de-blocking algorithm in section III.D and adaptive de-blocking filter for mobile video in

section III.E. As shown in figure 2, whole frame is first divided in 8x8 blocks in all

algorithms as described above expect H.264/AVC algorithm. Then as shown in figure 3, 8x8

DB is composed from four adjacent 8x8 blocks.

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Figure. 2. 8x8 block in frame

Figure. 3. 8x8 DB in frame

A. Novel De-blocking Algorithm

This algorithm find out the activity across block boundaries and divide the whole frame in

three region (i) Smooth region (ii) Complex region and (iii) Intermediate region by using

threshold T1 and T2. Then different de-blocking filters are applied in frame according to the

region types. Hence whole algorithm can be divides in mainly four steps, filtering mode

decision by finding activity, smooth region filtering, complex region filtering and

intermediate filtering. The overall novel algorithm is as shown in figure 4.

Figure.4. Novel de-blocking algorithm

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B. Offset-Shift Algorithm

The overall offset and shift algorithm is as shown in figure 6. Figure 5 shows the 8x8 DB

with position of each pixel qi,j.

Figure.5. De-blocking block (DB) and pixel positions in DB [8]

For each DB, Horizontal activity (ACTH) and Vertical Activity (ACTV) are calculated as

shown in equation (2).

Figure.6. Offset and shift de-blocking algorithm

where ΔCk gives the cumulative addition of absolute difference of pixels belonging from kth

and kth+1 column, and ΔRk gives the cumulative addition of absolute difference of pixels

belonging from kth row and kth+1 row of 8×8 DB as defined in equation (3). The meaning of

ΔC and ΔR is depicted in figure 7.

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Figure.7. Activities of DB (a) Horizontal (b) Vertical [8]

After computing activity for all DB its average Avg_ACTH (Average Horizontal Activity)

and Avg_ACTV (Average Vertical Activity) is computed. Avg_ACTH and Avg_ACTV are

considered as T1 and T2 respectively and threshold is calculated as shown in equation(4).

This threshold is different for each frame, and varies according to motion content. Based on

threshold, DBs are classified into four types as condition described in Table 1.

C. H.264/AVC De-blocking Filter [3]

Figure.8. H264/AVC de-blocking algorithm[1]

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This algorithm generally is used in H.264/AVC encoding standard. The overall de-blocking

filter proposed in [3] is as shown in figure 8.

Boundary-Strength (Bs) parameter is assigned an integer value from 0 to 4 to every edge

between two 4 x 4 luminance sample blocks. Table 2 shows how the value of Bs depends on

the block modes and coding conditions of the two adjacent blocks. In this table, conditions

are evaluated from top to bottom, until one of the conditions holds true, and the

corresponding value is assigned to Bs. Here Bs determines the filtering mode performed on

the edge.

Table 2 Filtering mode and boundary strength [3]

The filter process should follow the specific filtering order from the top-left MB to the

bottom-right one. Each MB consists of one 16x16 luminance block and two 8x8 chrominance

blocks.

Figure.9. Boundary strengths for (a) for luma and (b) for chroma edges

The de-blocking filter process consists of a horizontal filtering across all vertical edges and a

vertical filtering across all horizontal edges. Figure 9(a) and 9(b) shows the edges to be

filtered for a luminance block and a chrominance block, respectively.

D. Directional De-blocking Algorithm

The overall algorithm is as shown in figure 10. As described in figure 11 and figure 12,in this

algorithm each horizontal and vertical 1-D array of pixels of whole DB is classified in three

region type: smooth region, intermediate region and complex region by finding activity A(v)

of each 1-D pixels array using same equation (3.1) and (3.2) . Now the region type which is

classified maximum numbers of time during classification of each 1-D array of pixels is

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considered as the whole region type for DB in horizontal direction and vertical direction as

described in figure 12. Thus each DB has two region types, one in horizontal direction and

other in vertical direction. Now each DB is classified in SDB (Smooth De-blocking Block),

DDB and CDB based on the horizontal and vertical region type as described in table 3. CDB

further can be classified in texture de-blocking block and edge de-blocking block. DDB

further classified in HDB and VDB. After the block classification, different filtering methods

on each DB type are applied in same way as was applied in offset and shift de-blocking

algorithm.

Figure.10. Directional de-blocking filter

Figure.11. Activity of all eights 1-D array of pixels of DB in direction (a) Horizontal (b)

Vertical

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Table Error! No text of specified style in document. Classification of de-blocking blocks

based on region type

Figure.12. Region type decision in DB

E. Adaptive De-blocking Technique for Mobile Video

This algorithm is divided mainly in three steps, pre-processing, activity measurement and

determination of filtering mode based on activity. The overall algorithm is as shown in figure

13. Pre-processing: Pre-processing distinguishes between original details and the undesired

signals in frame, and then it applies a low-pass filter only to the undesired signals. Hence

main purpose of pre-processing is to remove undesired high frequency signals from frame.

Elimination of these undesired signals in pre-processing, avoids the false filtering mode

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selection. Activity Measurement: To deal with various block types, calculate activity of P1,

F (P1) and activity of P2, F (P2) separately where P1 and P2 are the pixel arrays near block

boundary as define in figure 10.

Figure.13. Adaptive de-blocking algorithm for mobile video [9]

IV. EXPERIMENTAL RESULTS

Standard test images: Lena, Peppers, Barbara, Cameraman, Mandril, Boat, Filnstones,

Livingroom, Pirate, Woman_blonde and Woman darkhair [13] with resolution 512x512 and

standard CIF video sequences: foreman, crew, akiyo, news pamphlet, city and soccer [14]

with 300 frames at frame rate 30fps are used to evaluate the performance of the offset-shift

algorithm and novel algorithm. All standard images are compressed at different bitrates using

VCDemo [15] software and all video sequences were motion compensated and compressed

using H.264 transforming and quantization method [16]. Then performance of the both de-

blocking algorithm was measured in PSNR (dB) using MATLAB as a simulation tool. Figure

14 shows the experimental setup for all four algorithms.

Figure.14. Experimental setup

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PSNR is used to calculate objective quality of the decompressed frame of the video sequence

which is defined as equation (4.1). Where M is the number of samples on and rn are the gray

levels of the original and the reconstructed frames, respectively.

The structural similarity (SSIM) index [19] is a method for measuring the similarity between

two images (original image X and filtered image Y). SSIM index is a decimal value between

-1 and 1. Its value is 1 when both images are same and -1 when both images are different. It

is found by using equation (4.2).

A. Results of Novel De-blocking Algorithm

All video sequences are motion compensated using full search motion estimation and

transform coded using H.264 transform and quantization method [16]. All standard images

are also compressed at different QP using same H.264 transform and quantization method

[16]. Novel algorithm proposed in [10] apply 1-D filter or 2-D filter only in middle boundary

pixels of two adjacent 8x8 block of frame for horizontal filtering and vertical filtering. Now

filtering procedure is slightly modified by applying 1-D filter or 2-D filter to all boundary

pixels. Table 4 and table 5 show the PSNR and SSIM for standard video sequences and test

images respectively. Result shows that novel algorithm with modified filtering procedure

give better PSNR (dB) compared to novel algorithm proposed in [10]. Here both novel de-

blocking algorithm is tested on 300 frames of each video sequence. Figure 15 and Figure 16

shows the PSNR and SSIM before filter, after filter[10] and after novel algorithm with

modified filtering procedure of each video sequences for all 300 frames. Table 4 shows the

average PSNR over 300 frames for all above three cases. For experiment different novel

algorithm parameters are set as S=2, T1=2, T2=3, λ =8 and Th=QP. Small value of S is taken

to detect small activity and T1=2 to avoid false smooth region rejection.

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Table Error! No text of specified style in document. PSNR and SSIM in Video sequences

for novel algorithm

Figure.15. PSNR of video sequences in novel algorithm (a) foreman (b) crew(c) akiyo (d)

news (e)pamphlet (f) city

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Figure.16. SSIM of video sequences for novel de-blocking filter (a) foreman (b) crew(c)

akiyo (d) news (e)pamphlet (f) city

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Table 5 PSNR and SSIM in standard test images for novel algorithm

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Figure.17. PSNR Improvement (dB) for Lena at different QP

Fig.17 shows the improvement in PSNR (dB) for standard test image lena at different value

of QP. It shows that PSNR improvement increases as the QP increases which means that

novel algorithm with modified filtering procedure is more suitable for highly compressed

images. Same thing is also true for all other standard test images.

B. Results of Offset-Shift Algorithm

This algorithm is also tested on 300 frames of each video sequence. Table 7 and table 8

shows the average PSNR for compressed video sequences for different values of threshold. In

separate T method two thresholds T1 and T2 are used which are average of horizontal

activity and average of vertical activity of all DBs respectively. In average T method only one

threshold is used which is average of T1 and T2. In maximum and minimum threshold

method, maximum and minimum value of T1 and T2 is selected and called TMAX and

TMIN respectively. Finally performance is evaluated with 75%, 25%, 50%, and 10% of

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TMIN. Figure 18 and Figure 19 shows the PSNR before filter, after filter [8] and after offset-

shift algorithm with 50% of TMIN for all 300 frames of each video sequence. Table 9 shows

the PSNR for compressed images at different bit rate 0.1, 0.2 and 0.3 bpp for different values

of threshold.

Table 6 PSNR of Video Sequences for offset-shift adaptive threshold

Figure.18. PSNR of video sequences in offset-shift algorithm (a) foreman (b) crew(c)

akiyo (d) news (e)pamphlet (f) city

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Figure.19. SSIM of video sequences in offset-shift algorithm (a) foreman (b) crew(c)

akiyo (d) news (e) pamphlet (f) city

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Table 7 PSNR of Video Sequences for offset-shift adaptive minimum threshold

Table 8 SSIM of Video Sequences for offset-shift adaptive minimum threshold

Figure.20. CIF foreman frame (a) Original (b) Reconstructed at QP=38 (c) Filtered

using fix T=28 method (d) Filtered using proposed 50% of TMIN

Figure.21. Lena 512x512 (a) Original (b) compressed with 0.2bpp (c) Filtered using fix

T=28 method (d) Filtered using proposed 50% of TMIN

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Result in table 6, table 7 and table 8 shows that algorithm with adaptive threshold taken as

50% of TMIN gives better improvement in PSNR (dB) compared to algorithm with non-

adaptive fix threshold T=28[8]. Although average T method provides better improvement in

PSNR for standard images compressed at 0.1, 0.2 and 0.3 bpp compared to 50% of TMIN,

but if we consider overall improvement for standard test images, which are compressed at 0.1

to 0.7 bpp then 50% of TMIN method provides better improvement. figure 20(a) shows frame

of moving sequence foreman with CIF resolution, figure 20(b) indicates reconstructed frame

after motion compensation, transform and quantize with QP=38. This reconstructed frame is

filtered using fix T=28 and 50% of TMIN and shown in figure 20(c) and figure 20(d)

respectively. Figure 21(a) shows standard test image Lena with 512×512 resolutions, figure

21(b) indicates compressed image with 0.2bpp. This compressed image is filtered using fix

T=28 and 50% of TMIN and shown in figure 21(c) and 21(d) respectively.

Table 9 PSNR and SSIM of standard test image for offset-shift algorithm

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C. Results of Directional De-blocking Filter

In this algorithm also same experimental setup is used as was used in previous two de-

blocking algorithm.

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Table 10 PSNR & SSIM in video sequences for directional de-blocking algorithm

Also all same standard video sequences are used to evaluate performance of this algorithm.

Table 10 shows PSNR and SSIM for proposed directional de-blocking filter. Table 11 and

Table 12 clearly demonstrate that proposed directional de-blocking filter gives better PSNR

and SSIM compared to filter proposed in [10], method for novel filter and averaging de-

blocking filter.

D. Results of Adaptive algorithm for Mobile Video

In this algorithm also same experimental setup is used as was used in previous two de-

blocking algorithm.

Table 11 PSNR and SSIM of standard test image for Adaptive algorithm for Mobile

Video

E. Comparison

Table 12 Comparative analysis for PSNR improvement

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Table 13 Comparative analysis for SSIM improvement

V. CONCLUSION

De-blocking filter can be classified in two categories, in-loop filter and post filter. Post filter

are better compared to in-loop filter because post-processing requires no modifications of

existing standards. Only one disadvantage of post filter is that, it requires additional frame

buffer to hold filtered frame.

Earlier proposed novel algorithm was not able to remove blocking artifacts efficiently as it

apply 1-D filter or 2D filter only in middle boundary pixels of two adjacent 8x8 block of

frame for horizontal as well vertical filtering. Novel algorithm with modified filtering

procedure has the capacity to effectively reduce the blocking artifacts of the transform coded

as well as highly compressed images or frames. Offset and shift algorithm uses fixed

threshold T to classify DBs in to UDB, HDB, VDB and CDB. In this report, method of

computation of adaptive threshold is proposed based on frame content and activity, which is

used to classify DBs. In comparison of these algorithms, proposed directional de-blocking

filter gives better PSNR and SSIM compared to filter proposed method for novel filter and

averaging de-blocking filter.

ACKNOWLEGEMENT

I would like to thank Prof. Vinesh Kapadia, Head, Department of Electronics and

Communication Engineering and Prof. Zaid M. Shaikhji, Assistant Professor & M.E

Coordinator, Department of Electronics and Communication Engineering, S. N. Patel

Institute of Technology & Research Centre, Umrakh, Bardoli, who took great efforts in

providing good guidance and were enthusiastic about my work. I learnt from them the

patience and persistence required to analyze one’s own work and address peer review

comments. I owe them a great debt of gratitude.

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