A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT Background. <s> A new image compression algorithm is proposed, based on independent embedded block coding with optimized truncation of the embedded bit-streams (EBCOT). The algorithm exhibits state-of-the-art compression performance while producing a bit-stream with a rich feature set, including resolution and SNR scalability together with a random access property. The algorithm has modest complexity and is extremely well suited to applications involving remote browsing of large compressed images. The algorithm lends itself to explicit optimization with respect to MSE as well as more realistic psychovisual metrics, capable of modeling the spatially varying visual masking phenomenon. <s> BIB001 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT Background. <s> Embedded block coding with optimized truncation (EBCOT) is the most important technology in the latest image-coding standard, JPEG 2000. The hardware design of the block-coding engine in EBCOT is critical because the operations are bit-level processing and occupy more than half of the computation time of the whole compression process. A general purpose processor (GPP) is, therefore, very inefficient to process these operations. We present detailed analysis and dedicated hardware architecture of the block-coding engine to execute the EBCOT algorithm efficiently. The context formation process in EBCOT is analyzed to get an insight into the characteristics of the operation. A column-based architecture and two speed-up methods, sample skipping (SS) and group-of-column skipping (GOCS), for the context generation are then proposed. As for arithmetic encoder design, the pipeline and look-ahead techniques are used to speed up the processing. It is shown that about 60% of the processing time is reduced compared with sample-based straightforward implementation. A test chip is designed and the simulation results show that it can process 4.6 million pixels image within 1 s, corresponding to 2400 /spl times/ 1800 image size, or CIF (352 /spl times/ 288) 4 : 2 : 0 video sequence with 30 frames per second at 50-MHz working frequency. <s> BIB002 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT Background. <s> We propose a hardware accelerator IP for the Tier-1 portion of Embedded Block Coding with Optimal Truncation (EBCOT) used in the JPEG2000 next generation image compression standard. EBCOT Tier-1 accounts for more than 70% of encoding time due to extensive bit-level processing. Our architecture consists of a 16-way parallel context formation module and a 3-stage pipelined arithmetic encoder. We reduce power consumption by properly shutting down parts of the circuit. Compared with the known best design, we reduce 17% of the cycle count and reach a level within 5% of the theoretical lower bound. We have implemented the design in synthesizable Verilog RTL with an AMBA-AHB interface for SOC design. FPGA prototyping has been successfully demonstrated and substantial speedup achieved. <s> BIB003 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT Background. <s> This work presents a parallel context-modeling coding architecture and a matching arithmetic coder (MQ coder) for the embedded block coding (EBCOT) unit of the JPEG2000 encoder. The tier-1 of the EBCOT consumes most of the computation time in a JPEG2000 encoding system, and the proposed parallel architecture can increase the throughput rate of the context-modeling. To match the high throughput rate of the parallel context-modeling architecture, and efficient pipelined architecture for context-based adaptive arithmetic encoder is proposed. This encoder of JPEG2000 can work at 185MHz to encode one symbol each cycle. Compared with the conventional context-modeling architecture, our parallel architecture can decrease the execution time about 25%. <s> BIB004 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT Background. <s> JPEG2000 is the state-of-the-art compression standard. Its high performance is achieved by using EBCOT algorithm. In this paper, we present two methods to improve the computation efficiency, hardware utilization and area reduction for pass-parallel context formations (CF) of EBCOT. The first one called Sample-Parallel Pass-Type Decision (SPPD) method improves the performance in deciding the pass types of all four samples in the same column, while the second one called Column-Based Pass-Parallel Coding (CBPC) method codes all four samples in the same column concurrently. Simulation results based on TSMC CMOS 0.15μm process indicate that the CF architecture based on the proposed techniques reduces 13.83% of the encoding time, 18.28% of the hardware cost, and increase 34.78% of the hardware utilization, compared to the original pass-parallel CF. <s> BIB005 </s> A Survey of Image Compression Algorithms for Visual Sensor Networks <s> EBCOT Background. <s> This is nothing less than a totally essential reference for engineers and researchers in any field of work that involves the use of compressed imagery. Beginning with a thorough and up-to-date overview of the fundamentals of image compression, the authors move on to provide a complete description of the JPEG2000 standard. They then devote space to the implementation and exploitation of that standard. The final section describes other key image compression systems. This work has specific applications for those involved in the development of software and hardware solutions for multimedia, internet, and medical imaging applications. <s> BIB006
EBCOT is a block-based encoding algorithm, where each subband (or block) is divided into nonoverlapping blocks of DWT coefficients called code blocks. Every code block is coded independently, which allows to generate a separate highly scalable embedded bitstream, rather than producing a single bitstream representing the whole image. As reported in BIB001 , EBCOT, which represents the core functioning of the standard JPEG2000, is divided into two processes called Tier-1 and Tier-2, as shown in Figure 6 . The data inputs of the Tier-1 process are code blocks while the outputs are bitstreams. Tier-1 is responsible for context formation and arithmetic encoding of the bitplane data and generates embedded block bitstreams. Context formation scans all code block pixels in a specific way as explained in BIB001 . The context formation requires three passes: significance propagation pass, magnitude refinement pass, and clean-up pass. Arithmetic encoding module encodes the code block data according to their contexts generated during context formation. Tier-2 operates on the bitstreams generated from Tier-1 to arrange their contributions in different quality layers. This is performed according to rate-distortion optimized property and features specified by the user. At the end of the second tier, a compressed bitstream is generated for transmission purpose. EBCOT is a scalable and efficient compression algorithm, robust against error transmission, and has a flexible organization and arrangement of bitstreams BIB001 . Nevertheless, the EBCOT algorithm requires additional memory requirement, which increases the power dissipation and the computational complexity. Precisely, EBCOT uses two tiers (Tier-1 and Tier-2) to code information, which needs long time processing and high power consumption. In particular, context formation phase which includes three passes takes a long time to encode samples for a code block BIB005 . It is observed in BIB006 that Tier-1 is the most computational intensive part, due to the fact that it requires significant bit-level processing and three separate passes through the code blocks. It is reported in BIB003 that Tier-1 accounts for more than 70% of encoding time, due to the extensive bit-level processing, followed by the DWT transformation stage (see Table 1 for an example of both lossless and lossy compressions). Recently, efficient techniques have been suggested to improve the coding speed and to minimize the memory usage of EBCOT, for example, BIB005 BIB002 BIB004 . Almost all of them focus on the enhancement of the context formation phase by different ways. As our goal is not to survey all the techniques suggested in the general domain of digital imaging, we provide some research works that can be used to minimize the EBCOT power consumption in VSN. For instance, two speed-up methods called sample skipping and group-of-column skipping were proposed to accelerate the encoding process of EBCOT BIB002 . Another interesting architecture is proposed in BIB004 , where the authors merge the three coding passes into a single pass in order to improve the overall system performance as well as to reduce memory requirement. Further details on this subject can be found in BIB005 BIB002 BIB004 .