Visual Quality Enhancement in DCTDomain Spatial Downscaling Transcoding
Using Generalized DCT Decimation.

Presented by:
M...
Agenda
•

Introduction



Definition
What is meant by:






Transcoding
Spatial Domain & DCT Domain
Downscaling
Al...
Agenda
•

Computation Reduction Using Sparse matrix
representation

•

Analysis of the proposed DCT decimation filter

•

...
Visual Quality Enhancement in DCT-Domain Spatial
Downscaling Transcoding Using Generalized DCT
Decimation.

•The goal imag...
Abstract
1. we propose a generalized discrete cosine transform
(DCT) decimation scheme for DCT-domain spatial
downscaling ...
Abstract (cont.)
3.We compare
the filtering performances and computational complexities of the
proposed scheme and the pix...
What is meant by:


Transcoding :

Video transcoding is an operation
of converting a video bit-stream into from one forma...
Spatial Domain & DCT Domain
Spatial Domain (Image Enhancement):

Definition:
is manipulating or changing an image represen...
Discrete Cosine Transform (DCT) domain
•This allows us to discard those equations involving the higher
frequency component...
Aliasing:
• When a signal is under-sampled, aliasing can result
•Aliasing is when high frequency components masquerade as ...
Quantization: is the process of converting a continuous analog
audio signal to a digital signal with discrete numerical va...
•In realizing a transcoder, the computational cost and the
picture quality are usually the two most important concerns.
A...
MC: reduces the temporal redundancy.
DCT: reduces the spatial redundancy and achieves energy compaction

Quantization i...
II. Generalized DCT decimation
for spatial downscaling
Formulation of generalized DCT
decimation
Formulation of generalized
DCT decimation
STEP 1:
A group of consecutive 8-samples DCT vectors are
first transformed into ...
Formulation of generalized
DCT decimation
The N-pixel vector is then transformed
into its corresponding DCT vector by Npoi...
Formulation of generalized
DCT decimation
The N-point DCT representation of fN can be computed
by:

fN: N-pixel vector tha...
Formulation of generalized
DCT decimation
Formulation of generalized
DCT decimation
STEP 2:
DCT decimation is subsequently performed on
the N-sample DCT vector by e...
Formulation of generalized
DCT decimation
Formulation of generalized
DCT decimation
STEP 3:
The N/2-Pixel vector is transformed into a group
of consecutive 8-Sample...
Formulation of generalized
DCT decimation
•Computation reduction using
sparse matrix representations

•Analyses of DCT-DECIMATION
downscaling filters


To reduce computation for matrix operations in
(4) and (7)

can be represented in sparse matrix form
The following characteristics have been noted in
with dimension (N/2) * 8:
1.
General case:
The entries of r th row in
are...
Based on the previous facts:

are defined to reduce computations ,
For i = 1, …., k/2 where k is even :

Substituting in
...







The operation of retaining the low-frequency coefficients of a DCT subframe and taking the half-size IDCT is, i...




The linear transform can be represented as
an N-band filter bank structure
the z-transform of the output y can be
ob...
N increases, the gain of
DCT decimation
filters, |F0(z)|, becomes
much flatter in the low
frequency part
As

(0~π/2), whe...
N

•increasing the sub-frame size for the
DCT decimation filters will lead to
better quality of downscaled image
but it wi...








We use:
One CIF (352* 288)
Two ITUR(704*576)
In each video 150 frame
Encoded by front-end MPEG-2 encoder
Eac...






Resulting in a spatially downscaled video
of quarter size
We implement bilinear filter and 7-tap
gaussian filter
...


The optimal least- squares upscaling filter
matrix minimize the error between
original sized image & its reconstructed
...







Steps:
Divide each downscaled pixel vector
into N/2 sample pixel vector then
transform it to N/2 sample DCT vec...






We compare PSNR values of o/p image of
downscaling filter followed by upscaling
filter & final o/p of transcoders...






We experiment results in N=8,16,32

In N=16,32 we have better visual quality
over that N=8 but in the same time w...
Thank You !
Quality enhamcment
Quality enhamcment
Quality enhamcment
Quality enhamcment
Upcoming SlideShare
Loading in …5
×

Quality enhamcment

328 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
328
On SlideShare
0
From Embeds
0
Number of Embeds
3
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • This cascaded architecture isflexible and can be used for bitrate adaptation, spatial andtemporal resolution-conversion without drift. It is, however,computationally intensive for real-time applications , eventhough the motion-vectors and coding-modes of the incomingbit-stream can be reused for fast processing.
  • A cascaded DCT-domaindownscaling transcoder (CDDT) architecture was first proposedin [5] as depicted in Fig. 1, where a bilinear filtering scheme wasused for the spatial resolution downscaling in the DCT domain.
  • As N increases, the gain of DCTdecimation filters, |F0(z)|, becomes much flatter in the low frequencypart (0~π/2), whereas the gain decreases rapidly in thehigh-frequency part (π/2~π). For the bilinear filter, the gain inthe high-frequency part is always larger than its counterparts ofDCT decimation filter and 7-tap filter. The smaller gain in thehigh-frequency part implies less visible aliasing artifacts in thedownscaled image.
  • Quality enhamcment

    1. 1. Visual Quality Enhancement in DCTDomain Spatial Downscaling Transcoding Using Generalized DCT Decimation. Presented by: Marwa Ahmed Mona Ragheb Sara Serag Yara Ali
    2. 2. Agenda • Introduction   Definition What is meant by:      Transcoding Spatial Domain & DCT Domain Downscaling Alias Quantization   • Video Adaptation Frequency Synthesis Generalized DCT Decimation For Spatial Downscaling
    3. 3. Agenda • Computation Reduction Using Sparse matrix representation • Analysis of the proposed DCT decimation filter • Experimental results  Optimal list squares Up-Scaling filters ( Steps ) • Peak Signal to Noise ratio • Conclusion
    4. 4. Visual Quality Enhancement in DCT-Domain Spatial Downscaling Transcoding Using Generalized DCT Decimation. •The goal image enhancement is to improve the image quality so that the processed image PSNR is high and less computational complexity
    5. 5. Abstract 1. we propose a generalized discrete cosine transform (DCT) decimation scheme for DCT-domain spatial downscaling which performs two-fold decimation on subframes of a flexible size larger than the traditional 8 ᵡ block size to improve the visual quality. 8 2.Efficient sparse-matrix: representations are then derived to reduce the computation of the proposed DCT decimation method.
    6. 6. Abstract (cont.) 3.We compare the filtering performances and computational complexities of the proposed scheme and the pixel-domain downscaling schemes Our analysis shows that :  proposed scheme can reduce the aliasing artifact compared to the pixel-domain downscaling schemes, Where as the computational complexity may be increased We also:  integrate the proposed decimation scheme into the cascaded DCT-domain transcoder for spatial downscaling of a pre encoded video into its quarter size  Experiments show the proposed approach can achieve better visual quality than the existing schemes
    7. 7. What is meant by:  Transcoding : Video transcoding is an operation of converting a video bit-stream into from one format into another format (e.g., bit-rate , frame-rate, spatial resolution, and coding syntax). It is an efficient means of achieving fine and dynamic video adaptation.  Video adaptation : convert the video bit rate according to the channel conditions. Since the preencoded video is encoded at high quality and bit rate. For low bandwidth connections, the video bit rate needs to be converted to low bit rate.
    8. 8. Spatial Domain & DCT Domain Spatial Domain (Image Enhancement): Definition: is manipulating or changing an image representing an object in space to enhance the image for a given application. •Techniques are based on direct manipulation of pixels in an image •Used for filtering basics, smoothing filters, sharpening filters, unsharp masking and laplacian
    9. 9. Discrete Cosine Transform (DCT) domain •This allows us to discard those equations involving the higher frequency components, reducing the size of the equation set considerably. •in the DCT domain, each equation’s significance is dependent on the corresponding DCT frequency •Does not affect the compressibility of the original image because it enhance the image in the decompression
    10. 10. Aliasing: • When a signal is under-sampled, aliasing can result •Aliasing is when high frequency components masquerade as low frequency ones, and can result from improper image sampling Downscailing : The operation of retaining the low-frequency coefficients of aDCT sub-frame and taking the half-size IDCT Each N M sub-frame is extracting only the (N/2) (M/2) low-frequency.
    11. 11. Quantization: is the process of converting a continuous analog audio signal to a digital signal with discrete numerical values. Frequency synthesis : downscaling method first synthesizes an incoming macroblock consisting of four 8 ᵡ DCT blocks into 8 one 16 ᵡ DCT block, and then obtains the downscaled 8 ᵡ 16 8 DCT block by extracting the 8 ᵡ low-frequency DCT coefficients 8 of the 16 ᵡ DCT block 16
    12. 12. •In realizing a transcoder, the computational cost and the picture quality are usually the two most important concerns. A cascaded DCT-domain transcoder (CDDT): as depicted in Fig. 1, was first proposed in for spatial downscaling where a DCT-domain bilinear filter was used as the anti aliasing filter for the spatial downscaling. •cascade a decoder followed by an encoder. This cascaded pixel-domain architecture is flexible and can be used for bit rate adaptation and spatio-temporal resolution conversion without drift.
    13. 13. MC: reduces the temporal redundancy. DCT: reduces the spatial redundancy and achieves energy compaction Quantization is performed to achieve higher compression ratio. Variable-length coding.  VLC: is applied after the quantization to reduce the remaining redundancy.  decoder decodes the compressed input video  encoder re encodes the decoded video into the target format A video picture is predicted from its reference pictures and only the prediction errors are coded. the encoder reuses the motion vectors along with other information extracted from the input video bit stream.
    14. 14. II. Generalized DCT decimation for spatial downscaling
    15. 15. Formulation of generalized DCT decimation
    16. 16. Formulation of generalized DCT decimation STEP 1: A group of consecutive 8-samples DCT vectors are first transformed into an N-pixel vector by 8-point IDCT, Where N is a multiple of 8.
    17. 17. Formulation of generalized DCT decimation The N-pixel vector is then transformed into its corresponding DCT vector by Npoint DCT
    18. 18. Formulation of generalized DCT decimation The N-point DCT representation of fN can be computed by: fN: N-pixel vector that’s composed of 8-pixel vectors bi , i= 1……, N/8 TN: N-point DCT transform matrix that’s divided into N/8 columns of submatrices TN,i of size Nx8
    19. 19. Formulation of generalized DCT decimation
    20. 20. Formulation of generalized DCT decimation STEP 2: DCT decimation is subsequently performed on the N-sample DCT vector by extracting the N/2 low-frequency DCT coefficients followed by N/2point IDCT to obtain a downscaled N/2-pixel vector
    21. 21. Formulation of generalized DCT decimation
    22. 22. Formulation of generalized DCT decimation STEP 3: The N/2-Pixel vector is transformed into a group of consecutive 8-Sample DCT vectors by 8-Point DCT to form the output DCT array
    23. 23. Formulation of generalized DCT decimation
    24. 24. •Computation reduction using sparse matrix representations •Analyses of DCT-DECIMATION downscaling filters
    25. 25.  To reduce computation for matrix operations in (4) and (7) can be represented in sparse matrix form
    26. 26. The following characteristics have been noted in with dimension (N/2) * 8: 1. General case: The entries of r th row in are all zeros except the r th entry where r = 0, N/8, 2N/8, 3N/8 About N/8 of the entries are zeros. 2. Special case 1: For K = N/8 is even  Where i = 1, …….., N/16 r = 0, ……, N/2 and c = 0, …, 7 3. Special case 2: for K = N/8 is odd Where i = 1, …….., N/16 r = 0, ……,, N/2 and c = 0, …, 7 for matrix with i = N/16 + 1 , the entries of odd values of r + c is zero for r ≠ 0, N/8, 2N/8 , 3N/8 at most half of the entries are zeros.
    27. 27. Based on the previous facts:  are defined to reduce computations , For i = 1, …., k/2 where k is even : Substituting in :
    28. 28.     The operation of retaining the low-frequency coefficients of a DCT subframe and taking the half-size IDCT is, in effect, to perform anti-aliasing filtering and then followed by downsampling on the sub-frame in the pixel domain. Following is the analysis of the performances and complexities of various downscaling filters for the 1-D case. For N samples of 1-D signal x, when downscaled by a factor of two, the downscaled N/2-sample signal y is obtained as follows: The downscaling filter is defined as : Which is considered as a linear filter.
    29. 29.   The linear transform can be represented as an N-band filter bank structure the z-transform of the output y can be obtained by:
    30. 30. N increases, the gain of DCT decimation filters, |F0(z)|, becomes much flatter in the low frequency part As (0~π/2), whereas the gain decreases rapidly in the highfrequency part (π/2~π). For the bilinear filter, the gain in the high-frequency part is always larger than its counterparts of DCT decimation filter and 7-tap filter. The smaller gain in the high-frequency part implies less visible aliasing artifacts the magnitude responses of the two pixel-domain in the downscaledfilter filters: the bilinear image. and the 7-tap filter, and the generalized DCT decimation filters with N = 8, 16, 72, and 288
    31. 31. N •increasing the sub-frame size for the DCT decimation filters will lead to better quality of downscaled image but it will also increase the computational complexity significantly. •The shown table lists computational complexities with different N values and different filter: Tab length Bilinear 7-tab Gaussian Avg. Multiplicati ons 8 Avg. additions 56 48 8 Average computational complexity for pixel-domain downscaling scheme Avg. Multiplications Gen. DCT decima tion Avg. additions Sparse Gen. Sparse matrix DCT matrix decom decima decom positio tion positio n n 2788 4208 2792 352 4224 32 384 228 368 232 16 192 100 176 104 8 64 20 64 20 Average computational complexity using generalized DCT-decimation scheme
    32. 32.       We use: One CIF (352* 288) Two ITUR(704*576) In each video 150 frame Encoded by front-end MPEG-2 encoder Each coded video is transcoded by using CDDT
    33. 33.    Resulting in a spatially downscaled video of quarter size We implement bilinear filter and 7-tap gaussian filter Each downscaled image is decoded and up-scaled to its original size
    34. 34.  The optimal least- squares upscaling filter matrix minimize the error between original sized image & its reconstructed (downscaled & then upscaling)
    35. 35.     Steps: Divide each downscaled pixel vector into N/2 sample pixel vector then transform it to N/2 sample DCT vector. Expand the size of each N/2 sample DCT to N sample by padding zero coefficient in high frequency bands Apply N-point IDCT
    36. 36.    We compare PSNR values of o/p image of downscaling filter followed by upscaling filter & final o/p of transcoders. PSNR, is an engineering term for the ratio between the maximum possible power of a signal and the power of corrupting noise . So if PSNR is high the noise is low so the quality is high PSNR= 20 log10 (255/ RMSE)
    37. 37.    We experiment results in N=8,16,32 In N=16,32 we have better visual quality over that N=8 but in the same time we increased computational complexity So we can use N=8 in low activity region & N=16,32 in high activity region to achieve good trade-off between computational complexity & visual quality.
    38. 38. Thank You !

    ×