Next generation image compression standards: JPEG XR and AIC

5,445 views

Published on

Invited talk at Mobile Multimedia/Image Processing, Security, and Applications 2009, SPIE Defense, Security and Sensing Symposium, Orlando, FL, April 13-17, 2009

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

No Downloads
Views
Total views
5,445
On SlideShare
0
From Embeds
0
Number of Embeds
1,632
Actions
Shares
0
Downloads
0
Comments
0
Likes
3
Embeds 0
No embeds

No notes for slide
  • Mode 2: Best visual quality at high compression ratios where second stage blockiness may be apparent in Mode 1
  • Next generation image compression standards: JPEG XR and AIC

    1. 1. 1 Multimedia Signal Processing Group Swiss Federal Institute of Technology Next generation imageNext generation image compression standards:compression standards: JPEG XR and AICJPEG XR and AIC Touradj Ebrahimi Touradj.Ebrahimi@epfl.chTouradj.Ebrahimi@epfl.ch Invited paper at: Mobile Multimedia/Image Processing, Security, and ApplicationsInvited paper at: Mobile Multimedia/Image Processing, Security, and Applications 2009, SPIE Defense, Security and Sensing Symposium, Orlando, FL, April 13-17,2009, SPIE Defense, Security and Sensing Symposium, Orlando, FL, April 13-17, 2009.2009.
    2. 2. 2 Multimedia Signal Processing Group Swiss Federal Institute of Technology • Introduction • Overview of JPEG XR • Codec evaluations and discussions • Overview of AIC Outline of the presentation
    3. 3. 3 Multimedia Signal Processing Group Swiss Federal Institute of Technology • JPEG image compression has been among the most successful standards ever: – Used in digital photography, Internet imaging, … – Based on a 25 years old technology (DCT, Huffmann coding, …) • JPEG 2000 image compression has been proven to be the most efficient image compression standard ever: – Used in digital cinema, remote sensing, … – Based on a more recent technology (Wavelets, Arithmetic coding, …) • Why yet another image compression standard? Introduction
    4. 4. 4 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR overview • Under standardization by JPEG committee (currently at FDIS level) • Starting point is Microsoft’s proprietary compression scheme HD Photo • Mainly aimed at digital photography applications where JPEG 2000 penetration has been limited due to complexity reasons • Emphasis on High Dynamic Range (HDR) imaging
    5. 5. 5 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR positioning vs JPEG and JPEG 2000 Complexity Performance JPEG JPEG 2000 JPEG XR
    6. 6. 6 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR algorithm vs JPEG and JPEG 2000
    7. 7. 7 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR compression algorithm • JPEG XR is built around 2 central innovations 1. Reversible lapped biorthogonal transform (LBT) 2. Advanced coefficient coding • Otherwise, it is using rather “traditional” components – Color conversion – Transform – Quantization – Coefficient prediction – Coefficient scanning – Entropy coding 7
    8. 8. 8 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR transform • JPEG XR transform consists of 2 building blocks – Core transform (PCT) which is similar to a 4x4 DCT – Overlap transform (POT) which is shifted to reduce blockiness 4x4 block 4x4 block 8
    9. 9. 9 Multimedia Signal Processing Group Swiss Federal Institute of Technology • Hierarchical transform – Two transform stages – second stage operates on DC of 4x4 blocks of first stage • 3 overlap modes supported – Mode 0 or no overlap, which reduces to 2 stages of 4x4 core (block) transform  Lowest complexity mode – Mode 1 – overlap operator applied only to full resolution data, and not to second stage  Best R-D performance – Mode 2 – overlap operator applied at both resolutions  Best visual quality at very high compression JPEG XR transform T Transform of DC coefficients T 9
    10. 10. 10 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR quantization • “Harmonic” quantization scale – Defined by the set {Q} U {(Q+16) 2k }, for Q = 0…15 • Transform coefficients are equal norm by design – No additional scaling required during (de)quantization to compensate for unequal norms QP remapping 1 10 100 1000 10000 0 20 40 60 80 100 120 140 160 180 10
    11. 11. 11 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR coefficient prediction • JPEG XR uses DCAC prediction – Three levels of prediction 1. Prediction of DC values of second stage transform (DC subband) 2. Prediction of DCAC values of second stage transform (lowpass subband) 3. Prediction of DCAC values of first stage transform (highpass subband) 11
    12. 12. 12 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR coefficient prediction • Coefficient prediction rules – DC prediction  Null, top, left and mixed (mean of top and left) allowed  Only within-tile prediction – Lowpass prediction  Null, top and left allowed  Top and left need dominant edge signatures to be picked – Highpass prediction  Null, top and left allowed  Dominant direction indicated by lowpass values  Only within macroblock prediction Left prediction of highpass DCAC showing within macroblock prediction 12
    13. 13. 13 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR coefficient scanning • The process of converting the 2D transform into a linear encodable list – Also referred to as zigzag scan • JPEG XR scan order is adaptive – Changes as data is traversedAround 3% savings in bits over fixed scan orders 13
    14. 14. 14 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR entropy coding • Adaptive coefficient normalization – Handles high-variance transform coefficient data – Key observation: X=Exponential(λ) H=4.4393 bits X/8 H=1.4970 bits X mod 8 H=2.9423 bits Why not use fixed length codes? Alphabet of 8 ⇒ H=3 bits Overall entropy = 4.4970 bits 14
    15. 15. 15 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR entropy coding • Adaptive coefficient normalization – Triggered when nonzero transform coefficients happen frequently – When triggered, additional bit stream layer “Flexbits” is generated  Flexbits is sent “raw”, i.e. uncoded  For lossless 8 bit compression, Flexbits may account for more than 50% of the total bits  Flexbits forms an enhancement layer which may be omitted or truncated 15
    16. 16. 16 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR entropy coding SYMBOL Code 0 Code 1 Code 2 Code 3 Code 4 0 0000 1 0010 11 001 010 1 00 0001 0 0010 001 11 1 2 000 0000 00 0000 000 0000 000 0000 000 0001 3 000 0001 00 0001 000 0001 0 0001 0001 4 0 0100 0011 0 0001 0 0010 000 0010 5 010 010 010 010 011 6 0 0101 0 0011 000 0010 000 0001 0000 0000 7 1 11 011 011 0010 8 0 0110 011 100 0 0011 000 0011 9 0001 100 101 100 0011 10 0 0111 0 0001 000 0011 00 0001 0000 0001 11 011 101 0001 101 0 0001 SYMBOL Code 0 Code 1 0 010 1 1 0 0000 001 2 0010 010 3 0 0001 0001 4 0 0010 00 0001 5 1 011 6 011 0 0001 7 0 0011 000 0000 8 0011 000 0001 SYMBOL Code 0 0 10 1 001 2 0 0001 3 0001 4 11 5 010 6 0 0000 7 011 SYMBOL Code 0 Code 1 0 01 1 1 10 01 2 11 001 3 001 0001 4 0001 0 0001 5 0 0000 00 0000 6 0 0001 00 0001 SYMBOL Code 0 Code 1 Code 2 Code 3 0 1 01 0000 0 0000 1 0 0000 0000 0001 0 0001 2 001 10 01 01 3 0 0001 0001 10 1 4 01 11 11 0001 5 0001 001 001 001 SYMBOL Code 0 Code 1 0 1 1 1 01 000 2 001 001 3 0000 010 4 0001 011 SYMBOL Code 0 0 1 1 01 2 001 3 000 All code tables used in JPEG XR for coefficient and coded block pattern coding, enumerated in binary 16
    17. 17. 17 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR bitstream layout • Bitstream consists of header, index table and tile payloads – Index table points to start of tile payloads – Index entries can be up to 64 bits • Bitstream laid out in spatial or frequency mode – All tile payloads in an image are in the same mode – In spatial mode, macroblock data is serialized left to right, top to bottom – In frequency mode, tile payload is separated into four bands – DC, lowpass, highpass and flexbits 17
    18. 18. 18 Multimedia Signal Processing Group Swiss Federal Institute of Technology • Codec performance evaluation in terms of: – Compression efficiency. – Computational requirements. – Additional functionalities. Codec performance evaluation • Rate-Distortion (RD) curves = quality measure vs bit per pixel Original picture Output picture JPEG or JPEG 2000 or JPEG XR HUMAN SUBJECT (subjective QA) or FR METRIC (objective QA)
    19. 19. 19 Multimedia Signal Processing Group Swiss Federal Institute of Technology THERE ARE NOT YET RELIABLE and STANDARD OBJECTIVE METHODS FOR IMAGE QUALITY ASSESSMENT (QA) • Image and video systems complexity • Human Visual System (HVS) complexity • Lack of standardization Objective QA can be performed to provide a first comparison of a wide range of conditions. Subjective QA needs to be performed as benchmark, to validate the results of the objective metrics. Status
    20. 20. 20 Multimedia Signal Processing Group Swiss Federal Institute of Technology Objective QAObjective QA • Test materialTest material • Codecs and configuration parametersCodecs and configuration parameters • Quality metricsQuality metrics • Selected resultsSelected results
    21. 21. 21 Multimedia Signal Processing Group Swiss Federal Institute of Technology Test Material – 24 bpp pictures (sample pictures from Thomas Richter dataset, 2 different spatial resolutions: 3888x2592, 2592x3888 ) (sample pictures from Microsoft dataset, 6 different spatial resolutions: 4064x2704, 2268x1512, 2592x1944, 2128x2832, 2704x3499, 4288x2848)
    22. 22. 22 Multimedia Signal Processing Group Swiss Federal Institute of Technology JPEG XR vs JPEG2000 vs JPEG: • JPEG XR (DPK version 1.0):  one level overlapping and two level overlapping.  4:4:4 and 4:2:0 chroma subsampling. • JPEG 2000 (Kakadu version 6.0):  default settings (64x64 code-block size, 1 quality layer, no precincts, 1 tile, 9x7 wavelet, 5 decomposition levels).  rate control.  no visual frequency weighting and visual frequency weighting.  4:4:4 and 4:2:0 chroma subsampling. • JPEG (IJG version 6b):  default settings (Huffman coding).  default visually optimized quantization tables.  4:4:4 and 4:2:0 chroma subsampling. Codecs and configuration parameters
    23. 23. 23 Multimedia Signal Processing Group Swiss Federal Institute of Technology Different JPEG XR implementations: • JPEG XR DPK version 1.0:  different quantization steps for different color channels (default).  same quantization steps for different frequency bands (default). • JPEG XR Reference Software version 1.0:  same quantization steps for different color channels (default).  same quantization steps for different frequency bands (default). • JPEG XR Reference Software version 1.2 ‐ i.e. Thomas Ricther’s version:  different quantization steps for different color channels (same as DPK).  different quantization steps for different frequency bands (default).  new POT (leakage fix described in wg1n4660) (default). • JPEG XR Microsoft implementation described in HDPn21 / wg1n4549 :  different quantization steps for different color channels (enhanced encoding techniques  described in HDPn21 / wg1n4549) (default).  different quantization steps for different frequency bands (enhanced encoding techniques of HDPn21 / wg1n4549) (default).  new POT (leakage fix described in wg1n4660) (default). Codecs and configuration parameters
    24. 24. 24 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 1: Maximum Pixel Deviation (Linf) Linf R= max [abs(ImaR(x,y)-ImbR(x,y))] • Considering RGB color space: where: Ima , Imb = pictures to compare Linf G= max [abs(ImaG(x,y)-ImbG(x,y))] Linf B= max [abs(ImaB(x,y)-ImbB(x,y))] (Linf ∈ [0,1])
    25. 25. 25 Multimedia Signal Processing Group Swiss Federal Institute of Technology • PSNR evaluation considering: – R, G and B components – Y’, Cb and Cr components (ITU-R Rec. BT.601) MSE )12( log10PSNR 2B 10 − = ∑∑ = = −= M 1y N 1x 2 ba y)](x,Imy)(x,[Im MN 1 MSEwhere: M, N = image dimensions Ima , Imb = pictures to compare B= bit depth Metric 2: single channel PSNR
    26. 26. 26 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 3: PSNR weighted average (WPSNR) WPSNR = w1PSNR1 + w2PSNR2 + w3PSNR3 1/3w1/3,w1/3,w 321 === • PSNR considering weighted summation of the PSNRs evaluated on R, G and B components or Y’, Cb and Cr components (ITU-R Rec. BT.601): where: , considering R,G, and B components. , considering Y’, Cb, and Cr components. 101080 321 .w,.w,.w ===
    27. 27. 27 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 3: PSNR weighted average (WPSNR_MSE) )MSEwMSEwMSE(w 1)(2 10log 332211 2B 10 ++ − =WPSNR_MSE • PSNR considering weighted summation of the MSEs evaluated on R, G and B components or Y’, Cb and Cr components (ITU-R Rec. BT.601): where: , considering R,G, and B components. , considering Y’, Cb, and Cr components. 1/3w1/3,w1/3,w 321 === 101080 321 .w,.w,.w ===
    28. 28. 28 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 3: PSNR weighted average (WPSNR_PIX) WPSNR_PIX • PSNR considering MSE evaluated on weighted summation of the image R, G and B components: ( ) ( )[ ]∑∑= = ++−++ − = M y N x bbbaaa B )y,x(Imw)y,x(Imw)y,x(Imw)y,x(Imw)y,x(Imw)y,x(Imw MN )( log 1 1 2 332211332211 2 10 1 12 10 where: M, N = image dimensions Ima , Imb = pictures to compare B= bit depth , considering R,G, and B components. , considering Y’, Cb, and Cr components. 1/3w1/3,w1/3,w 321 === 101080 321 .w,.w,.w ===
    29. 29. 29 Multimedia Signal Processing Group Swiss Federal Institute of Technology  Estimate of luminance = mean intensity: Metric 4: Mean SSIM (MSSIM) (I) ∑∑= = =µ M 1y N 1x )y,xIm( MN 1 2/1 M 1y N 1x 2 ))y,x(Im( 1MN 1         µ− − =σ ∑∑= =  Estimate of contrast = standard deviation: σ µ− )(Im [1] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image Quality Assessment: From Error Measurement to Structural Similarity” (2004).  Estimate of picture structure: • Structural information = “attributes that represent the structure of objects in the scene, independent of the average luminance and contrast”.
    30. 30. 30 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 4: Mean SSIM (MSSIM) (II) 1 2 2 2 1 121 21 C C2 )Im,(Iml +µ+µ +µµ = 2 2 2 2 1 221 21 C C2 )Im,(Imc +σ+σ +σσ = [ ] [ ] [ ]γβα = )Im,(Ims)Im,(Imc)Im,(Iml)Im,(ImSSIM 21212121 )0,0,0( >γ>β>α  Luminance comparison function: (C1=constant)  Contrast comparison function: (C2=constant)  Measure of structural similarity = correlation between and Structure comparison function: where 1 11 )(Im σ µ− 2 22 )(Im σ µ− 321 32,1 21 C C )Im,(Ims +σσ +σ = ∑∑= = µ−µ− − =σ M 1y N 1x 22112,1 ))y,x()(Im)y,x((Im 1MN 1 (C3=constant)
    31. 31. 31 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 4: Mean SSIM (MSSIM) (III) • The SSIM indexing algorithm is applied using a sliding window approach which results in a SSIM index quality map of the image. • The average of the quality map is called Mean SSIM index (MSSIM). • Weighted summation of MSSIM indexes evaluated on Y’, Cb and Cr components (Y’CbCr color space - Rec. ITU-R BT.601): MSSIM = wyMSSIMY + wCbMSSIMCb + wCrMSSIMCr where: .0.1w0.1,w0.8,w CrCbY === (MSSIM ∈ [0,1])
    32. 32. 32 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 5: Visual Information Fidelity – Pixel (VIF-P) (I) [2] H. R. Sheikh, A. C. Bovik “Image Information And Visual Quality” (2004). • “Image information measure that quantifies the information that is present in the reference image and how much this reference information can be extracted from the distorted image” using statistical approach. Natural image (source) Channel (distortion) HVS HVS C F E  Reference image (E) = output of a stochastic natural source that passes through HVS channel and is processed by the brain  Test image (F) = output of an image distortion channel that distorts the output of the natural source before it passes through the HVS channel
    33. 33. 33 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 5: Visual Information Fidelity – Pixel (VIF-P) (II) ( ) ( )zE, zF, VIF CI CI =  Natural image modeling in wavelet domain using Gaussian scale mixtures (GSMs)  Information that the brain could ideally extract from reference image = mutual information between C and E:  Corresponding information that could be extracted from test image = mutual information between C and F: ( )zE;CI  VIF-P is a new implementation in a multi-scale pixel domain: • computationally simpler than Wavelet domain version. • performance slightly worse than Wavelet domain version. ( )zF;CI where: z= source model parameters. (VIF ∈ [0,1] and VIF>1 if the test image is enhanced version of the original)
    34. 34. 34 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 6: PSNR-HVS-M (I) Block 8x8 of distorted image Block 8x8 of original image DCT of difference between pixel values Reduction by value of contrast masking MSEH calculation of the block [3] N. Ponomarenko, F. Silvestri, K. Egiazarian, M.Carli, J. Astola, and V. Lukin, “On between-coefficient contrast masking of DCT basis functions” (2007). • DCT coefficients of 8x8 pixel blocks X and Y are visually undistinguished if: Ew(X-Y) < max (Em(X), Em(Y)) where Ew(block) is the energy of DCT coefficients of the block weighted according to CSF and Em(block) is the masking effect of DCT coefficients of the block which depends upon Ew(block) and upon the local variances.
    35. 35. 35 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 6: PSNR-HVS-M (II) H 2B 10 MSE )12( log10MHVSPSNR − =−− [ ]∑∑∑∑= = = = ∆= 7-M 1i 7N- 1j 8 1m 28 1n cijH )n,m(T)n,m(XKMSEwhere: M, N = image dimensions K= constant = visible difference between DCT coefficient of the original image and distorted image 8x8 blocks , depending upon contrast masking Tc = matrix of correcting factors based on standard visually optimized JPEG quantization tables B= bit depth ij)n,m(X ∆
    36. 36. 36 Multimedia Signal Processing Group Swiss Federal Institute of Technology Metric 7: DC Tune [4] A. B. Watson, A. P. Gale, J. A. Solomon, and A. J. Ahumada JR., “DCTune: A Techinque For Visual Optimization Of DCT Quantization Matrices For Individual Images” (1994).  developed as a method for optimizing JPEG image compression by computing the JPEG quantization matrices which yields a designated perceptual error  model of perceptual error based upon DCT coefficients analysis, taking into account: • luminance masking. • contrast masking. • spatial error pooling. • frequency error pooling.
    37. 37. 37 Multimedia Signal Processing Group Swiss Federal Institute of Technology Selected results 4:4:4 – JPEG XR vs JPEG2000 vs JPEG Average over image dataset of PSNR values on R component: bpp (bits/pixel) bpp (bits/pixel) bpp (bits/pixel) on G component: on B component:
    38. 38. 38 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of PSNR values on Y’ component: on Cb component: on Cr component: bpp (bits/pixel) bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – JPEG XR vs JPEG2000 vs JPEG
    39. 39. 39 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of WPSNR values on Y’CbCr components:on RGB components: bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – JPEG XR vs JPEG2000 vs JPEG
    40. 40. 40 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of WPSNR-MSE values on Y’CbCr components:on RGB components: bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – JPEG XR vs JPEG2000 vs JPEG
    41. 41. 41 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of WPSNR-PIX values on Y’CbCr components:on RGB components: bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – JPEG XR vs JPEG2000 vs JPEG
    42. 42. 42 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of MSSIM values on Y’ component: on Cb component: on Cr component: bpp (bits/pixel) Selected results 4:4:4 – JPEG XR vs JPEG2000 vs JPEG
    43. 43. 43 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of VIF-P values on Y’ component only: bpp (bits/pixel) Selected results 4:4:4 – JPEG XR vs JPEG2000 vs JPEG
    44. 44. 44 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of PSNR values (one level POT) on R component: on G component: on B component: bpp (bits/pixel) bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – different JPEG XR implem.
    45. 45. 45 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of PSNR values (two levels POT) on R component: on G component: on B component: bpp (bits/pixel) bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – different JPEG XR implem.
    46. 46. 46 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of PSNR values (one level POT) on Y component: on Cb component: on Cr component: bpp (bits/pixel) bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – different JPEG XR implem.
    47. 47. 47 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of PSNR values (two levels POT) on Y component: on Cb component: on Cr component: bpp (bits/pixel) bpp (bits/pixel) bpp (bits/pixel) Selected results 4:4:4 – different JPEG XR implem.
    48. 48. 48 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of WPSNR_MSE values bpp (bits/pixel) bpp (bits/pixel) one level POT: two levels POT: Selected results 4:4:4 – different JPEG XR implem.
    49. 49. 49 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of MSSIM values (one level POT) on Y component: on Cb component: on Cr component: bpp (bits/pixel) Selected results 4:4:4 – different JPEG XR implem.
    50. 50. 50 Multimedia Signal Processing Group Swiss Federal Institute of Technology Average over image dataset of MSSIM values (two levels POT) on Y component: on Cb component: on Cr component: bpp (bits/pixel) Selected results 4:4:4 – different JPEG XR implem.
    51. 51. 51 Multimedia Signal Processing Group Swiss Federal Institute of Technology Subjective quality assessment • Subjective quality assessment is the ultimate proof of efficiency • JPEG committee is currently carrying out various subjective quality assessments to evaluate JPEG XR performance • This is an essential part of JPEG XR development which still remains to be completed
    52. 52. 52 Multimedia Signal Processing Group Swiss Federal Institute of Technology Overview of Advanced Image Coding • JPEG 2000 has become an International standard since almost a decade • A natural question to ask is whether after more than a decade, there is any new technologies that may bring a significant edge when compared to JPEG 2000, useful for applications? • JPEG committee adopted a two-phase approach towards AIC standardization.
    53. 53. 53 Multimedia Signal Processing Group Swiss Federal Institute of Technology Overview of Advanced Image Coding (AIC) • Phase 1: – Define a dataset of typical images – Define anchors (the best JPEG and JPEG 2000 compressed images) – Define objective and subjective evaluation criteria to assess the performance of any new codec when compared to JPEG, JPEG 2000) – Identify other useful features needed in applications and define criteria to evaluate their efficiency. – The result of Phase 1 will be put in a Technical Report and can be used by the scientific and technical communities to allow them to assess their codecs.
    54. 54. 54 Multimedia Signal Processing Group Swiss Federal Institute of Technology Overview of Advanced Image Coding • Phase 2: – Standardization of an AIC compression algorithm based on a call for proposals using the evaluation approach in the technical report of Phase 1. – Call for evidence has already been issued to make sure that the evaluation methodologies of phase 1 can cope with new technologies assessments. – Several inputs already received as potential technologies (X-lets, non-linear transform, moving pictures compression, …)
    55. 55. 55 Multimedia Signal Processing Group Swiss Federal Institute of Technology AIC positioning vs JPEG, JPEG 2000 and JPEG XR Complexity Performance JPEG JPEG 2000 JPEG XR AIC
    56. 56. 56 Multimedia Signal Processing Group Swiss Federal Institute of Technology Acknowledgements: Francesca De Simone (EPFL) Vittorio Baroncini (FUB) Thanos Skodras (EAP) Some of the slides on JPEG XR overview have been adapted from a Microsoft presentation describing HD Photo to JPEG committee

    ×