A Survey of Image Compression Algorithms for Visual Sensor Networks <s> SPECK Background. SPECK is introduced in <s> We propose an embedded hierarchical image coding algorithm of low complexity. It exploits two fundamental characteristics of an image transform -- the well defined hierarchical structure, and energy clustering in frequency and in space. The image coding algorithm developed here, apart from being embedded and of low complexity, is very efficient and is comparable to the best known low-complexity image coding schemes available today.© (1998) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. <s> BIB001 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> SPECK Background. SPECK is introduced in <s> We propose an embedded, block-based, image wavelet transform coding algorithm of low complexity. It uses a recursive set-partitioning procedure to sort subsets of wavelet coefficients by maximum magnitude with respect to thresholds that are integer powers of two. It exploits two fundamental characteristics of an image transform-the well-defined hierarchical structure, and energy clustering in frequency and in space. The two partition strategies allow for versatile and efficient coding of several image transform structures, including dyadic, blocks inside subbands, wavelet packets, and discrete cosine transform (DCT). We describe the use of this coding algorithm in several implementations, including reversible (lossless) coding and its adaptation for color images, and show extensive comparisons with other state-of-the-art coders, such as set partitioning in hierarchical trees (SPIHT) and JPEG2000. We conclude that this algorithm, in addition to being very flexible, retains all the desirable features of these algorithms and is highly competitive to them in compression efficiency. <s> BIB002 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> SPECK Background. SPECK is introduced in <s> We propose a new highly scalable wavelet transform-based image coder, called S-SPECK, on the extension of a well-known zero-block image coder SPECK, by achieving not only distortion scalability, resolution scalability, and region of interest (ROI) retrievability, but also excellent compression performance with very low computational complexity. Though new features have been introduced into S-SPECK, our coder is quite competitive with SPECK on compression performance (peak signal-to-noise ratio) and computational complexity (encoding and decoding times) at various bit rates for standard test images. A novel quality layer formatting method is implemented in S-SPECK, which is much simpler and faster than PCRD used in JPEG2000. Extensive experiments have verified all our claims for S-SPECK. <s> BIB003 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> SPECK Background. SPECK is introduced in <s> Abstract In this paper a variant of the set partitioned embedded block coder (SPECK) image compression called listless SPECK (LSK) is presented. LSK operates without lists and is suitable for a fast, simple hardware implementation. LSK has a fixed predetermined memory requirement about 50% larger than needed for the independent image. Instead of lists, a state table with two bits per coefficient is used to keep track of the block coding and the information that has been encoded. LSK sparsely marks selected block nodes of insignificant blocks in the state table, in such a way that large groups of predictably insignificant pixels are easily identified and skipped during the coding process. The image data is stored as a one-dimensional recursive zigzag array for computational efficiency and algorithmic simplicity. Performance of the proposed algorithm on standard test images is nearly same as SPECK. <s> BIB004 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> SPECK Background. SPECK is introduced in <s> SPIHT is an efficient image compression algorithm based on zerotrees. The significant wavelet coefficients are located by a series of set partitioning operations and then scalar quantized. Block-based algorithms inspired by SPIHT such as AGP and SWEET have good performance, but they are not embedded. Pearlman et al. proposed a block-based SPECK algorithm using set partitioning of embedded blocks to exploit the energy clustering characteristics of the coefficients while the bit stream remains embedded. We here propose a variation on SPECK using vector quantization to code the significant coefficients. Different VQ techniques including TSVQ and ECVQ are also considered. Vector SPECK shows a performance improvement over JPEG 2000 at the cost of added complexity <s> BIB005
During the initialization phase, a starting threshold T is chosen and the input image X is partitioned into two types of sets: S and I; see Figure 7 . The set S, which represents the root of the pyramid, is added to LIS. The set I represents the rest of the image, that is, I = X −S. In the second phase called sorting pass, a significance test against the current threshold is performed to sort each block of type S in LIS. If an S block is significant, it is divided by a quadtree partitioning process into four subsets as shown in Figure 8 . In turn, each of these four subsets is treated in the same way as a set of type S and processed recursively until the pixel level is reached. The insignificant sets are moved to LIS for further processing. Once the processing of sets S is achieved, a significance test against the same threshold is performed for I blocks. Thus, if an I block is significant, it is divided by the octave band partitioning scheme into four sets, one set having the same type I and three sets of type S; see Figure 9 . This new set I formed by this partitioning process is reduced in size. At the last phase, the refinement pass is started for LSP pixels, where the nth most significant bit (MSB) of pixels is output, at the exception of pixels which have been added during the last sorting pass. Finally, the threshold is halved, and the coding process (sorting and refinement passes) is repeated until the expected bit rate is achieved, or the set I will be empty. Many advantages of SPECK are observed. It has efficient performance compared to the other low complexity algorithms available today. In fact, it gives higher compression ratio, has relatively low dynamic memory requirements; employs progressive transmission, and has low computational complexity and fast encoding/decoding process, due to the inherent characteristics of the quadtree partitioning scheme. However, SPECK presents some minor disadvantages related mainly to the use of lists LIS and LSP, which require efficient memory management plan. In general, those lists grow up with the encoding process, which requires additional memory. This may be unattractive in hardware implementations. As with EZW and SPIHT, SPECK suffers from the unreliable network state and, thus, is vulnerable against packets loss which requires the use of an appropriate error correction scheme. Another shortcoming of SPECK is that it does not support resolution scalability BIB003 . In the last few years, some attempts to overcome SPECK shortcomings have been suggested in the literature, for instance BIB003 BIB004 BIB005 . In what follows, we list only some works whose applications seem useful in case of VSN. More complex SPECK-based algorithms such as Vector SPECK BIB005 are not reported. A listless variant of SPECK image compression called LSK is suggested in BIB004 . LSK uses the block-partitioning policies of SPECK and does an explicit breadth first search, without the need for lists as in BIB002 or BIB001 . State information is kept in an array of fixed size that corresponds to the array of coefficient values, with two bits per coefficient to enable fast scanning of the bit planes. The authors in BIB003 suggest another variant of SPECK called Scalable SPECK (S-SPECK), which extends the original SPECK to a highly scalable low complexity scheme. Adopting SPECK as a compression tool in power-constrained devices, such as visual sensors, might be a promising technique, due to its high compression ratio and low computational complexity. Its advantages over JPEG and EZW in terms of high compression ratio are less computational complexity and low power consumption, as well as less complex implementation which make it possible to play an interesting task in image compression for power-limited applications. Low-power image compression SPECK encoders are highly encouraged in VSN application. To the best of our knowledge, the integration of SPECK within a compression chain of a VSN has not yet been investigated. An open research work may be the implementation of SPECK-based coders dedicated to the power-constrained VSN. A listless version of SPECK, as in BIB004 , could be an efficient scheme to be implemented in visual sensors.