PUMA publications for /tag/Distortionhttps://puma.ub.uni-stuttgart.de/tag/DistortionPUMA RSS feed for /tag/Distortion2024-03-29T11:06:54+01:00Spatial Constant Quantization in JPEG XR is Nearly Optimalhttps://puma.ub.uni-stuttgart.de/bibtex/20ed9cbeb964eafd1c03a7a15e7023033/thomasrichterthomasrichter2016-03-10T09:18:49+01:00Annealing, Distortion JPEG Quantization, Rate Simulated Theory, Variable XR, coding, compression, data distortion image optimisation, quantisation rate theory, vector <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Richter" itemprop="url" href="/person/1f8ff101d1065b16a8b19dce9bdb0c1b3/author/0"><span itemprop="name">T. Richter</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Data Compression Conference (DCC), 2010</span>, </em></span><em>page <span itemprop="pagination">79-88</span>. </em><em>Snowbird, UT, </em><em>IEEE, </em>(<em><span>March 2010<meta content="March 2010" itemprop="datePublished"/></span></em>)</span>Thu Mar 10 09:18:49 CET 2016Snowbird, UTData Compression Conference (DCC), 2010mar79-88{S}patial {C}onstant {Q}uantization in {JPEG} {XR} is {N}early {O}ptimal2010Annealing, Distortion JPEG Quantization, Rate Simulated Theory, Variable XR, coding, compression, data distortion image optimisation, quantisation rate theory, vector The JPEG XR image compression standard, originally developed under the name
HD-Photo by Microsoft, offers the feature of spatial variably quantization;
its codestream syntax allows to select one out of a limited set of possible
quantizers per macro block and per frequency band. In this paper, an
algorithm is presented that finds the rate-distortion optimal set of quantizers,
and the optimal quantizer choice for each macro block. Even though it
seems plausible that this feature may provide a huge improvement for images
whose statistics is non-stationary, e.g. compound images, it is demonstrated that
the PSNR improvement is not larger than 0.3dB for a two-step heuristics
of feasible complexity, but improvements of up to 0.8dB for
compound images are possible by a much more complex optimization strategy.Universal Refinable Trellis Coded Quantizationhttps://puma.ub.uni-stuttgart.de/bibtex/2129ef12bf4cd76de2771395baf389fb4/thomasrichterthomasrichter2016-03-10T09:18:49+01:00(signal);trellis coded codes;bitplane coding;Image coding;codebook coding;embedded coding;progressive coding;residual compression;Decoding;Distortion distortion measurement;Image performance;scalar quantisation quantization quantization;Bit quantization;universal quantizer;trellis rate;Data reconstruction;Quantization;Rate refinable theory;Rate-distortion;Transform training;rate trellis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Steger" itemprop="url" href="/person/135306931e0653b50d572d8534660dde9/author/0"><span itemprop="name">S. Steger</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Richter" itemprop="url" href="/person/135306931e0653b50d572d8534660dde9/author/1"><span itemprop="name">T. Richter</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Data Compression Conference, 2009. DCC '09.</span>, </em></span><em>page <span itemprop="pagination">312-321</span>. </em>(<em><span>March 2009<meta content="March 2009" itemprop="datePublished"/></span></em>)</span>Thu Mar 10 09:18:49 CET 2016Data Compression Conference, 2009. DCC '09.mar312-321{U}niversal {R}efinable {T}rellis {C}oded {Q}uantization2009(signal);trellis coded codes;bitplane coding;Image coding;codebook coding;embedded coding;progressive coding;residual compression;Decoding;Distortion distortion measurement;Image performance;scalar quantisation quantization quantization;Bit quantization;universal quantizer;trellis rate;Data reconstruction;Quantization;Rate refinable theory;Rate-distortion;Transform training;rate trellis We introduce a novel universal refinable trellis quantization scheme (URTCQ) that is suitable for bitplane coding with many reconstruction stages. Existing refinable trellis quantizers either require excessive codebook training and are outperformed by scalar quantization for more than two stages (MS-TCQ, E-TCQ), require a huge computational burden (SR-TCQ) or achieve a good rate distortion performance in the last stage only (UTCQ). The presented quantization technique is a mixture of a scalar quantizer and an improved version of the E-TCQ. For all supported sources only one time training to an i.i.d. uniform source is required and its incremental bitrate is not more than 1 bps for each stage. The complexity is proportional to the number of stages and the number of trellis states. We compare the rate distortion performance of our work on generalized Gaussian i.i.d. sources with the quantizers deployed in JPEG2000 (USDZQ, UTCQ). It turns out that it is in no stage worse than the scalar quantizer and usually outperforms the UTCQ except for the last stage.Spatial Constant Quantization in JPEG XR is Nearly Optimalhttps://puma.ub.uni-stuttgart.de/bibtex/20ed9cbeb964eafd1c03a7a15e7023033/rainerreichelrainerreichel2016-03-03T17:45:04+01:00Annealing, Distortion JPEG Quantization, Rate Simulated Theory, Variable XR, coding, compression, data distortion image optimisation, quantisation rate theory, vector <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Thomas Richter" itemprop="url" href="/person/1f8ff101d1065b16a8b19dce9bdb0c1b3/author/0"><span itemprop="name">T. Richter</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Data Compression Conference (DCC), 2010</span>, </em></span><em>page <span itemprop="pagination">79-88</span>. </em><em>Snowbird, UT, </em><em>IEEE, </em>(<em><span>March 2010<meta content="March 2010" itemprop="datePublished"/></span></em>)</span>Thu Mar 03 17:45:04 CET 2016Snowbird, UTData Compression Conference (DCC), 2010mar79-88{S}patial {C}onstant {Q}uantization in {JPEG} {XR} is {N}early {O}ptimal2010Annealing, Distortion JPEG Quantization, Rate Simulated Theory, Variable XR, coding, compression, data distortion image optimisation, quantisation rate theory, vector The JPEG XR image compression standard, originally developed under the name
HD-Photo by Microsoft, offers the feature of spatial variably quantization;
its codestream syntax allows to select one out of a limited set of possible
quantizers per macro block and per frequency band. In this paper, an
algorithm is presented that finds the rate-distortion optimal set of quantizers,
and the optimal quantizer choice for each macro block. Even though it
seems plausible that this feature may provide a huge improvement for images
whose statistics is non-stationary, e.g. compound images, it is demonstrated that
the PSNR improvement is not larger than 0.3dB for a two-step heuristics
of feasible complexity, but improvements of up to 0.8dB for
compound images are possible by a much more complex optimization strategy.Universal Refinable Trellis Coded Quantizationhttps://puma.ub.uni-stuttgart.de/bibtex/2129ef12bf4cd76de2771395baf389fb4/rainerreichelrainerreichel2016-03-03T17:45:04+01:00(signal);trellis coded codes;bitplane coding;Image coding;codebook coding;embedded coding;progressive coding;residual compression;Decoding;Distortion distortion measurement;Image performance;scalar quantisation quantization quantization;Bit quantization;universal quantizer;trellis rate;Data reconstruction;Quantization;Rate refinable theory;Rate-distortion;Transform training;rate trellis <span data-person-type="author" class="authorEditorList "><span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Steger" itemprop="url" href="/person/135306931e0653b50d572d8534660dde9/author/0"><span itemprop="name">S. Steger</span></a></span>, </span> and <span><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="T. Richter" itemprop="url" href="/person/135306931e0653b50d572d8534660dde9/author/1"><span itemprop="name">T. Richter</span></a></span></span>. </span><span class="additional-entrytype-information"><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Data Compression Conference, 2009. DCC '09.</span>, </em></span><em>page <span itemprop="pagination">312-321</span>. </em>(<em><span>March 2009<meta content="March 2009" itemprop="datePublished"/></span></em>)</span>Thu Mar 03 17:45:04 CET 2016Data Compression Conference, 2009. DCC '09.mar312-321{U}niversal {R}efinable {T}rellis {C}oded {Q}uantization2009(signal);trellis coded codes;bitplane coding;Image coding;codebook coding;embedded coding;progressive coding;residual compression;Decoding;Distortion distortion measurement;Image performance;scalar quantisation quantization quantization;Bit quantization;universal quantizer;trellis rate;Data reconstruction;Quantization;Rate refinable theory;Rate-distortion;Transform training;rate trellis We introduce a novel universal refinable trellis quantization scheme (URTCQ) that is suitable for bitplane coding with many reconstruction stages. Existing refinable trellis quantizers either require excessive codebook training and are outperformed by scalar quantization for more than two stages (MS-TCQ, E-TCQ), require a huge computational burden (SR-TCQ) or achieve a good rate distortion performance in the last stage only (UTCQ). The presented quantization technique is a mixture of a scalar quantizer and an improved version of the E-TCQ. For all supported sources only one time training to an i.i.d. uniform source is required and its incremental bitrate is not more than 1 bps for each stage. The complexity is proportional to the number of stages and the number of trellis states. We compare the rate distortion performance of our work on generalized Gaussian i.i.d. sources with the quantizers deployed in JPEG2000 (USDZQ, UTCQ). It turns out that it is in no stage worse than the scalar quantizer and usually outperforms the UTCQ except for the last stage.