Low-complexity multiple ROI image coding method based on different degrees of interest

Regions of Interest (ROI) image coding enables the regions important to medical diagnosis to be encoded and transmitted at higher quality than the other regions. In the paper, a new ROI coding method called Compensation-based Partial Bitplane Alternating Shift (CPBA- Shift) is described. The proposed method divides all bitplanes of ROI and background (BG) coefficients into two portions-Alternating Shift Portion (ASP) and Compensation Shift Portion (CSP). In ASP, partial the most significant bitplanes of ROI and BG coefficients are shifted by bitplane-by-bitplane. In CSP, the least significant bitplanes of ROI and BG coefficients are scaled using compensation scheme according to the compression quality in ROI and BG. Simulation experiments show that the new method, in addition to alleviating the drawbacks of both ROI coding methods in JPEG2000, can support arbitrarily shaped multiple ROI coding with different degrees of interest without coding the shapes information.

Summary form only given. Normally, image data have important details in only some regions. One feature of JPEG2000 still image coding is the ability to code a region of interest (ROI) with higher quality. The standard defines two methods for ROI coding, generic shift and maximum bitplane shift (maxshift). Generic shift can select the scaling value of the ROI, but the shape needs to be sent to the decoder, whereas maxshift does not need to send it. However, maxshift does not allow the selection of a scaling value to define the relative importance between ROI and background (BG) coefficients. Later, BbB shift and GBbB shift were proposed. These two methods can select the scaling value without the need to send shape information, but these two methods are not applicable for multiple ROI coding. Lately, the partial significant bitplane (PSB) shift was proposed. It offers different degrees of interest and also supports multiple ROI coding. However, PSB shift sacrifices some qualities of ROI in exchange for the improved quality of the BG region. To some extent, it causes the ROI to be coded at lesser quality than the BG. We analyze the advantages and disadvantages of each method and propose the use of PSB shift in conjunction with the maxshift scheme to achieve better quality in the ROI with the ability to adjust the degree of interest relative to its importance.

Region of interest (ROI) coding is a mechanism deployed in several image coding systems to enable different degrees of coding priority to specific regions of the image. JPEG2000 standard provides two ROI coding methods. However, both of them are based in mechanisms that scale the quantized coefficients. This compels to encode the additional bit-planes needed for the scaling, causing a penalization in the overall coding performance, and the ROIs can not be modified without a complete re-encoding of the image. This paper introduces two ROI coding methods that use rate-distortion optimization techniques to prioritize arbitrary ROIs over the rest of the image without penalizing the overall coding performance. Experimental results suggest that the proposed methods achieve a close to optimal accuracy, improving the Implicit ROI coding method in terms of ROI rate distortion performance.

Three-dimensional wavelet coding of volumetric medical images provides better coding performance compared to corresponding 2D methods by exploiting the inter-slice correlation that exists in such data. It introduces however latencies when it comes to retrieving/transmitting specific parts of the volume. This paper presents an extension to 3D-SPIHT which allows 3D region of interest (ROI) coding. ROI coding enables faster reconstruction of diagnostically useful regions in volumetric datasets by assigning higher priority to them in the bitstream. It also introduces the possibility for an increased compression performance, by allowing certain parts of the image to be coded in a lossy manner while others are coded losslessly. The necessary modifications to 3D-SPIHT for ROI coding are described and methods for specifying a 3D ROI without adding a significant overhead are suggested. Results are presented highlighting the benefits of the ROI extension.

In this paper, a flexible region of interest (ROI) image coding method so-called partial block bitplane shift (PBBShift) is presented for JPEG2000. The new method offers two strategies - bitplane block scaling (BBS) and partial bitplane shift (PBS), which support arbitrarily shaped single or multiple ROI. In BBS, all bitplanes of ROI and background (BG) are divide into four parts - most significant ROI (MSR), general significant ROI (GSR), general significant BG (GSB), and least significant BG (LSB). In PBS, all bitplanes of MSR are shifted up over all residual bitplanes. The bitplanes in LSB are shifted down below all bitplanes. The bitplanes of GSR and GSB are not shifted. Experimental result shows that the presented method can flexibly adjust the compression quality between ROI and background by selecting an arbitrary scaling value without any ROI shape information. Additionally, PBBShift can efficiently code multiple ROIs with different degrees of interest in an image.

Region of interest coding in JPEG 2000

It takes a long time for the users to view a whole image from the image server under the low-bandwidth internet environments or in case of a big sized image. In this case, as there needs a technique that preferentially transfers a part of image, JPEG2000 offers a ROI(Region-of-Interest) coding. In ROI coding, the users see the thumbnail of image from the server and specifies some regions that they want to see first. And then if an information about the regions are informed to the server, the server preferentially transfers the regions of the image. The existing methods requested a huge time to compute the mask information, but this thesis approximately computes the regions and reduces the creating time of the ROI masks. If each code block is a mixed block which ROI and background are mixed, the proper boundary points should be acquired. Searching the edges of the block, getting the two points on the edge, to get the boundary point inside the code block, the method searches a mid point between the two edge points. The proposed method doesn`t have a big difference compared to the existing methods in quality, but the processing time is more speedy than the ones.

JPEG2000 offers the technique which compresses the interested regions with higher quality than the background. It is called by an ROI(Region-of-Interest) coding method. In this paper, we use images including the human faces, which are processed uppermost and compressed with high quality. The proposed method consists of 2 steps. The first step extracts some faces and the second one is ROI coding. To extract the faces, the method cuts or scale-downs some regions with window pixels for all the pixels of the image, and after preprocessing, recognizes the faces using neural networks. Each extracted region is identified by ROI mask and then ROI-coded using Maxshift method. After then, the image is compressed and saved using EBCOT. The existing methods searched the ROI by edge distributions. On the contrary, the proposed method uses human intellect. And the experiment shows that the method is sufficiently useful with images having several human faces.

Region-of-interest (ROI) image coding is one of the new features included in the JPEG2000 image coding standard, where two methods are defined, i.e., the Maxshift method and the generic scaling based method. In this paper, we propose a new JPEG2000 compliant ROI coding method called Most Significant Bitplane Shift (MSBShift) that combines the advantages of the two standard methods. The MSBShift method not only supports arbitrarily shaped ROI coding without coding the shape, but also enables the flexible adjustment of compression quality in the ROI and background. Additionally, the new method can efficiently code multiple ROIs with different priorities in an image.

This paper proposes a pan-sharpening method based on multi-direction subbands and deep neural networks. First, by utilizing the multi-scale and multi-direction properties of the nonsubsampled contourlet transform (NSCT), panchromatic (PAN) image is decomposed into the low frequency subbands in different resolutions and the high frequency subbands in different directions. Pan-sharpening method aims to fuse the high frequency subband coefficients of PAN image and the low frequency subband coefficients of multispectral (MS) image. Second, in order to better extract the feature of the high frequency subbands in different directions of PAN image, the deep neural network (DNN) is trained using the image patches of high frequency subbands of PAN image. Third, in the fusion stage, we exploit NSCT on the principal component of resampled low resolution (LR) MS image. The high frequency subbands of output high resolution (HR) MS image is obtained by forward propagation of the trained DNN, which input is the high frequency subbands of LR MS image. Finally, a new subband set is obtained by fusing the reconstructed high frequency subband and the original low frequency subband of LR MS image. The HR MS image is produced by executing the inverse transform of NSCT and adaptive PCA (A-PCA) on the new subband set. The experimental results show the proposed method outperforms other well-known methods in terms of both objective measurements and visual evaluation.

For purpose of improving the quality of multi-source fusion images, this paper introduces a fusion method of infrared and visible images based on the combination of the non-subsampled contourlet transform (NSCT) and the improved pulse coupled neural network model (PCNN). To begin with, registered images are decomposed by NSCT, the low frequency and high frequency sub-bands are also obtained. Then, the energy of Laplacian (SML) is used for the link intensity of PCNN, computing spatial frequency (SF) with a low frequency sub-band, and the gradient energy (EOG) with a high frequency sub-band. By means of treating them as external input of PCNN. Then fused coefficients are selected by sum of ignition output amplitude maximum, a fused image is finally obtained by inverse NSCT. Experimental results show that the fusion image has a high definition and effectively preserves information of the source image.

Differing from common 2D images, a texture map, since it is used to project onto a 3D model in 3D space, not only contains 2D texture information, but also implicitly associates certain 3D geometric information. Related to this, an effective 3D geometry-dependent texture map compression method with hybrid region of interest (ROI) coding is proposed in this paper. We regard the visually important area of the texture map as the ROI. To acquire the visually important areas of the texture map, we take into account information from both the 3D geometry and 2D texture maps, depicting the saliency of the textured model, distortion of the texture mapping, and boundary of the texture atlas. These visually important areas are expressed as a visual importance map. According to the particularity of the texture map, a hybrid ROI coding method that utilizes Max-Shift and an improved post compression rate distortion (PCRD) technique is presented, guided by this visual importance map. To find the exact wavelet coefficients pertaining to these ROIs before carrying out the hybrid ROI coding, this paper proposes a stochastic coefficient priority mask map computational method. Experimental results show that the visually important areas of the texture image have a better visual effect and that a good rendering result can be obtained from the texture mapping.

Recently, medical imaging has increased its importance greatly in picture archiving & communication (PACS). Many current compression schemes provide a very high compression rate but with considerable loss of quality. On the other hand, in some areas of image processing, in particular medical images, it may be sufficient to maintain high image quality only in the region of interest, i.e., in diagnostically important regions. ROI coding used in JPEG2000 (2002) has the capability of encoding a restricted region in an image prior to its background (BG) region. Its coding paradigm is based on the embedded block coding with optimised truncation (EBCOT) algorithm. In this paper, we propose an efficient, region-of-interest (ROI) coding scheme achieved by modifying the conventional ROI encoding method. This method implements a prioritized double ROI that adds an additional feature of viewing diagnostically significant (diseased region) and diagnostically insignificant but visually important surrounding region (region prone to infection).This requirement has been out looked in JPEG2000. From our experimental results it has been observed that there is a phenomenal increase in the compression efficiency in terms of bits per pixel (bpp) and PSNR compared to conventional JPEG2000 ROI.

The ability of a person to perceive image details falls precipitously with larger angle away from his visual focus. At any given bitrate, perceived visual quality can be improved by employing region-of-interest (ROI) coding, where higher encoding quality is judiciously applied only to regions close to a viewer's focal point. Straight-forward matching of viewer's focal point with ROI coding using a live encoder, however, is computation-intensive. In this paper, we propose a system that supports ROI coding without the need of a live encoder. The system is based on dynamic switching between two pre-encoded streams of the same content: one at high quality (HQ), and the other at mixed quality (MQ), where quality of a spatial region depends on its pre-computed visual saliency values. Distributed source coding (DSC) frames are periodically inserted to facilitate switching. Using a Hidden Markov Model (HMM) to model a viewer's temporal gaze movement, MQ stream is pre-encoded based on ROI coding to minimize the expected streaming rate, while keeping the probability of a viewer observing low quality (LQ) spatial regions below an application-specific ∊. At stream time, the viewer's gaze locations are collected and transmitted to server for intelligent stream switching. In particular, server employs MQ stream only if: i) viewer's tracked gaze location falls inside the high-saliency regions, and ii) the probability that a viewer's gaze point will soon move outside high-saliency regions, computed using tracked gaze data and updated saliency values, is below ∊. Experiments showed that video streaming rate can be reduced by up to 44%, and subjective quality is noticeably better than a competing scheme at the same rate where the entire video is encoded using equal quantization.

Three-dimensional wavelet coding of volumetric medical images provides better coding performance compared to corresponding 2D methods by exploiting the inter-slice correlation that exists in such data. It introduces however latencies when it comes to transmitting specific parts of the volume. This paper presents an extension to 3D-SPlHT which allows 3D region of interest (ROI) coding. ROl coding enables faster reconstruction of diagnostically useful regions in volumetric datasets by assigning higher priority to them in the bitstream. It also introduces the possibility for increased compression performance, by allowing certain parts of the volume to be coded in a lossy manner while others are coded losslessly. The necessary modifications to 3D-SPIHT for ROI coding are described and methods for specifying a 3D ROI without adding a significant overhead are suggested. Results are presented highlighting the benefits of the ROl extension.