IMAGE AND VIDEO DENOISING USING ADAPTIVE FILTER

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1 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 = 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 All rights reserved by ISSN :

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.

2 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 deblocking 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] All rights reserved by ISSN :

3 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 postprocessing 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 deblocking algorithm have been proposed to remove above all three kinds of noises. Deblocking 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. All rights reserved by ISSN :

(iii) Intermediate region by using threshold T1 and T2. Then different de-blocking filters are applied in frame according to the region types.

4 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 All rights reserved by ISSN :

5 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. All rights reserved by ISSN :

6 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] All rights reserved by ISSN :

7 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 All rights reserved by ISSN :

8 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 All rights reserved by ISSN :

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.

9 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 All rights reserved by ISSN :

10 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 deblocking 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 All rights reserved by ISSN :

11 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 deblocking 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. All rights reserved by ISSN :

PSNR of video sequences in novel algorithm (a) foreman (b) crew(c)

12 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 All rights reserved by ISSN :

Table 5 PSNR and SSIM in standard test images for novel

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.

15 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 All rights reserved by ISSN :

3 bpp for different values of threshold. Table 6 PSNR of Video Sequences for offset-shift adaptive threshold Figure.18.

16 TMIN. Figure 18 and Figure 19 shows the PSNR before filter, after filter [8] and after offsetshift 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 All rights reserved by ISSN :

Table 7 PSNR of Video Sequences for offset-shift adaptive

offset-shift adaptive minimum threshold Figure.20.

Filtered using fix T=28 method (d) Filtered using proposed

Lena 512x512 (a) Original (b) compressed with 0.

18 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 All rights reserved by ISSN :

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 nonadaptive fix threshold

19 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 nonadaptive 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 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 All rights reserved by ISSN :

C. Results of Directional De-blocking Filter In this algorithm also same experimental setup is used as

Table 10 PSNR & SSIM in video sequences for directional de-blocking algorithm Also all same standard video sequences are used to

Table 11 and Table 12 clearly demonstrate that proposed directional de-blocking filter gives better PSNR and SSIM compared to filter

Results of Adaptive algorithm for Mobile Video In this algorithm also same experimental setup is used as was used in previous two

21 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 deblocking filter. D. Results of Adaptive algorithm for Mobile Video In this algorithm also same experimental setup is used as was used in previous two deblocking 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 All rights reserved by ISSN :

Table 13 Comparative analysis for SSIM improvement V. CONCLUSION De-blocking filter can be classified in two categories, in-loop filter and post filter.

22 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. REFERENCES [01] Youn-Long Steve Lin,Chao-Yang Kao, Hung-Chih kuo, Hung-Chih Kuo, Jian-Wen Chen, VLSI Design for Video Coding H.264/AVC Encoding from Standard Specification to Chip. 1st edition, London, springer, [02] Jongho Kim and Chun-Bo Sim, Compression Artifacts Removal by Signal Adaptive Weighted Sum Technique, IEEE Transactions on Consumer Electronics, Vol. 57, No. 4, pp , Nov All rights reserved by ISSN :

23 [03] Peter List, Anthony Joch, Jani Lainema, Gisle Bjøntegaard, and Marta Karczewicz, Adaptive Deblocking Filter, IEEE Transactions on Circuits and Systems for Video Technology, vol.13, no.7, pp , Jul [04] ThomasWiegand, Gary J. Sullivan, Overview of the H.264/AVC Video Coding Standard IEEE Transactions On Circuits And Systems For Video Technology, Vol. 13, No. 7, pp , Jul [05] Hua-Chang Chung, Zong-Yi Chen, and Pao-Chi Chang, Low Power Architecture Design and Hardware Implementations of Deblocking Filter in H.264/AVC, IEEE Transactions on Consumer Electronics, Vol. 57, No. 2, pp , May [06] Tien-Ying Kuo, and Chen-Hung Chan, Fast Variable Block Size Motion Estimation for H.264 Using Likelihood and Correlation of Motion Field IEEE Transactions on Circuits and Systems For Video Technology, Vol. 16, No. 10,pp , Oct [07] Chen-Kuo Chiang, Wei-Hau Pan, Chiuan Hwang, Shin-Shan Zhuang, and Shang-Hong Lai, Fast H.264 Encoding Based on Statistical Learning, IEEE Transactions on Circuits and Systems For Video Technology, Vol. 21, No. 9, pp , Sep [08] Jongho Kim, Minseok Choi, and Jechang Jeong, Reduction of Blocking Artifacts for HDTV using Offset-and-Shift Technique, IEEE Transactions on Consumer Electronics, Vol. 53, No. 4, pp , Nov [09] Jongho Kim and Jechang Jeong, Adaptive Deblocking Technique for Mobile Video, IEEE Transactions on Consumer Electronics, Vol. 53, No. 4, pp , Nov [10] Shen-Chuan Tai, Yen-Yu Chen, and Shin-Feng Sheu, Deblocking Filter for Low Bit Rate MPEG-4 Video, IEEE Transactions On Circuits And Systems For Video Technology, VOL. 15, NO. 6, pp , June [11] Y. Lee, H. Kim, and H. Park, Blocking effect reduction of JPEG images by signal adaptive filtering, IEEE Trans. Image Process., vol. 7, no. 2, pp , Feb [12] S. Marsi, R. Castagno, and G. Ramponi, A simple algorithm for the reduction of blocking artifacts in images and its implementation, IEEE Trans. Consumer Electron., vol. 44, no. 3, pp , Aug [13] [Online].Available: [14] [Online].Available: [15] [Online].Available: [16] Iain E. Richardson, The H.264 Advanced Video Compression Standard, 2nd edition, UK, A John Wiley and Sons, Ltd., Publication, [17] Taeyoung Na and Munchurl Kim, A Novel No-Reference PSNR Estimation Method With Regard to Deblocking Filtering Effect in H.264/AVC Bitstreams IEEE Transactions On Circuits And Systems For Video Technology, Vol. 24, No. 2, Pp , February 2014 [18] Ramesh Kumar lama, Moo-Rak Choi, Post Processing Algorithm for reduction of Blocking Artifact in in HDTV, IEEE International Conference on Consumer Electronics (ICCE), pp ,2014 All rights reserved by ISSN :

Selective Intra Prediction Mode Decision for H.264/AVC Encoders

Selective Intra Prediction Mode Decision for H.264/AVC Encoders Jun Sung Park, and Hyo Jung Song Abstract H.264/AVC offers a considerably higher improvement in coding efficiency compared to other compression