TobepublishedinProceedingsofspieVol.4115.Seehttp://itSwww.epfl.ch/-dsanta/forthefinalreference which is based on LZ77 coupled with Huffman coding. PNG is capable of lossless compression only and supports gray scale, paletted color and true color, an optional alpha plane, interlacing and other features 2.5.JPEG2000 JPEG 20004, as noted previously, is the next Iso/ITU-T standard for still image coding. In the following, we restrict the description to Part I of the standard, which defines the core system. Part Il will provide various extensions for specific applications, but is still in preparation. JPEG 2000 is based on the discrete wavelet transform (DWT), scalar quantization, context modeling, arithmetic coding and post-compression rate allocation. The dwT is dyadic and can be performed with either the reversible Le gall (5, 3)taps filter, which provides for lossless coding, or the non-reversible Daubechies(9, 7)taps biorthogonal one, which provides for higher compression but does not do lossless. The quantizer follows an embedded dead-zone scalar approach and is independent for each sub-band. Each sub-band is divided into rectangular blocks(called code-blocks in JPEG 2000), typically 64x64, and entropy coded using context modeling and bit-plane arithmetic coding. The coded data is organized in so called layers, which are quality levels, using the post-compression rate allocation an output to the code-stream in packets. The generated code-stream is parseable and can be resolution, layer (i.e. SNR), position or component progressive, or any combination thereof. JPEG 2000 also supports a number of functionalities, many f which are inherent from the algorithm itself. Examples of this is random access, which is possible because of the independent coding of the code-blocks and the packetized structure of the codestream. Another such functionality is the possibility to encode images with arbitrarily shaped Regions of Interest(ROD). The fact that the subbands are encoded bitplane by bitplane makes it possible to select regions of the image that will precede the rest of the image in the codestream. By scaling the sub-band samples so that the bitplanes encoded first only contain ROl information and following bitplanes only contain background information. The only thing the decoder needs to receive is the factor by which the samples were scaled. The decoder can then invert the scaling based only on the amplitude of the samples. Other supported functionalities are error-resilience, random access, multicomponent images, palletized color, compressed domain lossless flipping and simple rotation, to mention a few 3. COMPARISON METHODOLOGY Although one of the major, and often only, concerns in coding techniques has been that of compression efficiency, it is not the only factor that determines the choice of a particular algorithm for an application. Most applications also require other features in a coding algorithm than simple compression efficiency. This is often referred to as functionalities. Examples of such functionalities are ability to distribute quality in a non-uniform fashion across the image(e.g, ROD), or resiliency to esidual transmission errors that occur in mobile channels. In this paper we report on compression efficiency, since it is still one of the top priorities in many imaging products, but we also devote attention to complexity and functionalities. In the next section we summarize the results of the study as long as the considered functionalities are concerned 3. 1. Compression efficiency Compression efficiency is measured for lossless and lossy compression. For lossless coding it is simply measured by the achieved compression ratio for each one of the test images. For lossy coding the root mean square error(RMSE)is used, as well as the corresponding peak signal to noise ratio(PSNR), defined as -20log10-2-1 RMSE where b is the bit depth of the original image Although RMSE and PSNR are known to not al ways faithfully represent visual quality, it is the only established, well- known, objective measure that works reasonably well across a wide range of compression ratios For images encoded with a Region of Interest(ROD)the RMSE, as well as the corresponding PSNR, are calculated both for the roi and for the entire image 3. 2. Complexity Evaluating complexity is a difficult issue, with no well-defined measure. It means different things for different applications It can be memory bandwidth, total working memory, number of CPU cycles, number of hardware gates, etc. FurthermoreTo be published in Proceedings of SPIE Vol. 4115. See http://ltswww.epfl.ch/~dsanta/ for the final reference. 3 which is based on LZ77 coupled with Huffman coding. PNG is capable of lossless compression only and supports gray scale, paletted color and true color, an optional alpha plane, interlacing and other features. 2.5. JPEG 2000 JPEG 20002 , as noted previously, is the next ISO/ITU-T standard for still image coding. In the following, we restrict the description to Part I of the standard, which defines the core system. Part II will provide various extensions for specific applications, but is still in preparation. JPEG 2000 is based on the discrete wavelet transform (DWT), scalar quantization, context modeling, arithmetic coding and post-compression rate allocation. The DWT is dyadic and can be performed with either the reversible Le Gall (5,3) taps filter9 , which provides for lossless coding, or the non-reversible Daubechies (9,7) taps biorthogonal one10, which provides for higher compression but does not do lossless. The quantizer follows an embedded dead-zone scalar approach and is independent for each sub-band. Each sub-band is divided into rectangular blocks (called code-blocks in JPEG 2000), typically 64x64, and entropy coded using context modeling and bit-plane arithmetic coding. The coded data is organized in so called layers, which are quality levels, using the post-compression rate allocation and output to the code-stream in packets. The generated code-stream is parseable and can be resolution, layer (i.e. SNR), position or component progressive, or any combination thereof. JPEG 2000 also supports a number of functionalities, many of which are inherent from the algorithm itself. Examples of this is random access, which is possible because of the independent coding of the code-blocks and the packetized structure of the codestream. Another such functionality is the possibility to encode images with arbitrarily shaped Regions of Interest (ROI)11. The fact that the subbands are encoded bitplane by bitplane makes it possible to select regions of the image that will precede the rest of the image in the codestream. By scaling the sub-band samples so that the bitplanes encoded first only contain ROI information and following bitplanes only contain background information. The only thing the decoder needs to receive is the factor by which the samples were scaled. The decoder can then invert the scaling based only on the amplitude of the samples. Other supported functionalities are error-resilience, random access, multicomponent images, palletized color, compressed domain lossless flipping and simple rotation, to mention a few. 3. COMPARISON METHODOLOGY Although one of the major, and often only, concerns in coding techniques has been that of compression efficiency, it is not the only factor that determines the choice of a particular algorithm for an application. Most applications also require other features in a coding algorithm than simple compression efficiency. This is often referred to as functionalities. Examples of such functionalities are ability to distribute quality in a non-uniform fashion across the image (e.g., ROI), or resiliency to residual transmission errors that occur in mobile channels. In this paper we report on compression efficiency, since it is still one of the top priorities in many imaging products, but we also devote attention to complexity and functionalities. In the next section we summarize the results of the study as long as the considered functionalities are concerned. 3.1. Compression efficiency Compression efficiency is measured for lossless and lossy compression. For lossless coding it is simply measured by the achieved compression ratio for each one of the test images. For lossy coding the root mean square error (RMSE) is used, as well as the corresponding peak signal to noise ratio (PSNR), defined as 2 1 10 20log − − b RMSE where b is the bit depth of the original image. Although RMSE and PSNR are known to not always faithfully represent visual quality, it is the only established, well￾known, objective measure that works reasonably well across a wide range of compression ratios. For images encoded with a Region of Interest (ROI) the RMSE, as well as the corresponding PSNR, are calculated both for the ROI and for the entire image. 3.2. Complexity Evaluating complexity is a difficult issue, with no well-defined measure. It means different things for different applications. It can be memory bandwidth, total working memory, number of CPU cycles, number of hardware gates, etc. Furthermore