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Encoding Stereo Images. Christopher Li, Idoia Ochoa and Nima Soltani. Outline. System overview Detailed encoder description Demonstration Results Extensions Conclusions. System Overview (Encoder). DWT. Quant. Arith Enc. L. DCT. Re-order. Arith Enc. u se ME. Motion Estimation.

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Encoding stereo images

Encoding Stereo Images

Christopher Li, IdoiaOchoa and NimaSoltani


Outline
Outline

  • System overview

  • Detailed encoder description

  • Demonstration

  • Results

  • Extensions

  • Conclusions


System overview encoder
System Overview (Encoder)

DWT

Quant

ArithEnc

L

DCT

Re-order

ArithEnc

use ME

Motion Estimation

residuals

R

Huff Enc

shift vectors

DWT

Quant

ArithEnc


Left image
Left Image

  • Daubechies-4 wavelet decomposition

    • 5 levels for luminance, 4 for chrominance

  • Uniform quantization with adaptive levels

    • Each component meets its own fraction of MSE

  • Arithmetic coding on the quantized residuals

    • Frequency tables are sent for each arithmetic coder


Left quantization
Left Quantization

  • Decomposed PSNR constraint

  • Allocated fractions of MSE to each color component

  • Met PSNR constraints by finding maximum uniform quantization levels that meet assigned MSEs


Left quantization motion estimation enable signal
Left QuantizationMotion Estimation Enable Signal

  • Heuristically choose differential vs. separate encoding of right image

Y wavelet coeffs

Quantize with

Calculate MSE

Yes

Encode differentially

No

Encode separately


Right image motion estimation block
Right ImageMotion Estimation Block

  • Partition into 30x30 blocks

  • Find shift vectors that minimize the MSE

  • Search an area from [-64,64] in the direction and [-6,6] in the direction for minimum distortion


Right image residual coding
Right ImageResidual coding

  • Impose residuals of Cb and Cr to be 0

    • Use remaining fraction of MSE for Y component

  • Compute DCT of block

    • Reshape using zig-zag ordering

    • Replace remaining zeros in block with end of block character

  • Perform arithmetic coding


Right image shift vector coding
Right ImageShift vector coding

  • Offline

    • Find joint statistics of the shift vectors over the training set

    • Construct Huffman table

    • During run-time, encode shift vectors using this Huffman table


Right image separately coded
Right ImageSeparately coded

  • Same method as left image

    • D4 wavelet, with 5 levels for Y, 4 for Cb, Cr

    • Uniform quantization with variable step

    • Arithmetic coding with frequencies sent


Writing to file
Writing to File

  • Unique quantization values encoded in header bits

    • Arithmetic coders

    • Encode frequencies, output length of sequence and sequence itself

  • Huffman encoders

    • Length of sequence and sequence itself

    • Tables stored offline


Decoder
Decoder

  • Perform all the steps of the encoder in reverse

    • Decode left image using inverse DWT

    • Read motion estimation flag for right image

      • If enabled, decode shift vectors and residuals

      • Else, decode using inverse DWT





Extensions
Extensions

  • Use intra-block coding for right image

  • Explore using different wavelets

    • Implement embedded zero trees in C

    • Explore run-length coding further

    • Apply uniform deadzonequantizers


Conclusions
Conclusions

  • Important trade-off between bits allocated to shift data and residual data

  • Arithmetic coding outperforms Huffman

  • Reshaping the DCT blocks allows us to use information, such as its size, to our advantage

  • Uniform quantizer is faster, simpler and has less overhead than Lloyd-max quantizers

  • MEX files reduce runtime significantly!


Thank you questions

Thank youQuestions?


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