Apple ProRes 422 is a high-quality compressed codec offering nearly all the benefits of Apple ProRes 422 HQ, but at 66 percent of the data rate for even better multistream, real-time editing performance. The target data rate is approximately 147 Mbps at 1920x1080 and 29.97 fps.
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Apple ProRes 422 LT is a more highly compressed codec than Apple ProRes 422, with roughly 70 percent of the data rate and 30 percent smaller file sizes. This codec is perfect for environments where storage capacity and data rate are at a premium. The target data rate is approximately 102 Mbps at 1920x1080 and 29.97 fps.
Apple ProRes 422 Proxy is an even more highly compressed codec than Apple ProRes 422 LT, intended for use in offline workflows that require low data rates but full-resolution video. The target data rate is approximately 45 Mbps at 1920x1080 and 29.97 fps.
Lung cancer sometimes develops on the wall of a giant emphysematous bulla (GEB). Herein, we describe a rare case in which lung cancer developed in lung tissue compressed by GEBs. A 62-year-old man underwent a computed tomography (CT) scan that revealed two right GEBs. A tumor was suspected in the highly compressed right upper lobe. Since the right bronchus was significantly shifted toward the mediastinum, it was difficult to perform a bronchoscopy. We inserted thoracic drains into the GEBs, and a subsequent CT scan revealed re-expansion of the remaining right lung and a 3.3 cm tumor in the right upper lobe. The shift of the right bronchus was improved, and bronchoscopy was performed. The tumor was diagnosed as non-small cell lung cancer (NSCLC). Additionally, the GEBs were found to have originated from the right lower lobe. We performed a right upper lobectomy, mediastinal lymph node dissection, and bullectomy of the GEBs via video-assisted thoracoscopic surgery. In preoperative evaluation of a GEB, assessing re-expansion and lung lesions of the remaining lung is important, and intracavity drainage of a GEB may be useful. KEY POINTS: Significant findings of the study Cancer that develops in lung tissue highly compressed by a giant emphysematous bulla is difficult to diagnose. In the preoperative evaluation of a giant emphysematous bulla, assessing re-expansion and lung lesions of the remaining lung is important. What this study adds After performing intracavity drainage of a giant emphysematous bulla, the remaining lung re-expands, and the bronchial shift improves; subsequently, bronchoscopy makes it possible to diagnose lung cancer in the remaining lung.
In this paper, we are proposing a compression-based JTC that detects multiple color targets where we have used largely compressed targets and/or reference images. We will prevent the usual false alarms correlation peaks from the output plane (a common issue when dealing with multiple targets detection) by using fringe-adjusted filter and reference phase-shifted and reference phase-encoded schemes. The proposed system detects multiple targets and references compressed up to a ratio of 94%. Many simulation experiments (with added Gaussian, Salt & Peppers noises as well as occluded reference images) are carried out to demonstrate the robustness, discrimination, and detection capability of the proposed scheme. In Section 2, we provide the theoretical discussion of the various JTC schemes. Section 3 presents the computer simulation experiments on the compressed colored images. Section 4 is a short conclusion of this work.
In this section, based on the previous discussions, we propose multiple targets recognition for color images when the targets and/or the reference images are greatly compressed and experienced under severe noise and occlusion conditions. Handling compressed images would make the JTC processor practically closer to near real-time processing and reduce the storage requirements as discussed in the introduction of this paper.
A three-channel (Red, Green, Blue) correlator is used to deal with colored images, where each channel will process separately one fundamental color component. Hence, all the equations in the previous section would represent the mathematics for one out of three fundamental color components. The output correlations of the three channels (see Equation (17)) are combined to generate the final correlation output to detect the colored targets. The input joint image is divided into two halves: the left half contains the input scene while the right half has the reference target. An illustrative example of the detection capability of the proposed scheme for colored images is presented in Figure 3. Figure 3(a) presents an input scene, prepared to have multiple identical targets images, multiple identical non targets images, and other images with various colors. The size of all colored images is 32 32 pixels and have JPEG format. Figures 3(b)-(d) show the correlation peaks when the target is the orange, red apple, and yellow pear, respectively. Next, we present simulation results for detecting colored targets that are exposed to severe compression and strong noise (Gaussian and Salt & Peppers) conditions. In addition, we tested our proposed detection scheme for references occluded up to 75%. Finally, we repeated the same experiment for compressing high-resolution color images. Note that Figure 3 demonstrated the detection of multiple targets when both the target and the reference images are uncompressed. Next, Figure 4(a) displays the successful recognition of uncompressed target image (Figure 3(a)) and a 94% compressed reference image. When the compression of the reference reaches 95%, the recognition fails as
shown in Figure 4(b). Now, the correlation results of the cases compressed target, uncompressed reference and compressed target, compressed reference are shown in Figure 5(a) and Figure 5(b), respectively.
The next simulation results will experiment the most challenging case of compression of both target and reference images, which are subjected to severe noise conditions. Figure 6 shows that the proposed JTC scheme successfully recognizes 94% compressed targets added to a random Gaussian noise with a
In many situations it is needed to recognize a region or a part of a target image. Consequently, the proposed JTC scheme is tested for 25%, 50%, and 75% occluded reference with noise-free 94% compressed images as shown in Figure 8. Further, 90% compressed noisy images correlated with 50% occluded reference image are displayed in Figure 9. As a matter of fact, when images are
It is well known that JPEG compression algorithm discards pixels that are not important in human eye perception such as small color variations and/or high-frequency components in color images. In this regard, we tested our proposed color multiple targets recognition scheme for higher resolution images such as the 128 128 pixels color images shown in Figure 10. The targets detection capability is excellent for noise-free images that are significantly compressed to 94% ratio as illustrated in Figure 10(a). However, this detection capability is
In comparison, the low-resolution 32 32 pixels images in Figure 6(a) and Figure 7(a) afforded Gaussian and Salt & Peppers noise densities of 0.2 and 0.3, respectively. The multiple targets recognition can be significantly improved by slightly decreasing the compression ratio of images. For instance, in Figure 11(a) and Figure 11(b), we used 90% compressed images instead of 94% ones. This has resulted in increasing the noise capability of the correlator to handle Gaussian noise density increase from 0.005 to 0.1 (20 folds improvement) while the Salt & Peppers noise density changes from 0.003 to 0.125 (41 folds improvement). In addition, Figure 11(c) and Figure 11(d) show more improvement in handling severe noise densities (Gaussian and Salt & Peppers densities = 0.4) when the image compression ratio decreases to 80%.
Furthermore, the simulations show that the performance of the proposed scheme for occluded high-resolution images is excellent for noise-free and up to 94% compressed color images (see Figure 12). Again, in order to support large amount of noises, the compression ratio must be lowered. An illustrative example is shown in Figure 13.
In this paper, we have demonstrated a compression-based FJTC target detection for colored images. First, we have demonstrated the detection capability of the proposed scheme for the same target with different colors. Then, we have used the reference phase-shifted technique to eliminate false alarms and zero-order terms due to multiple desired and undesired multiple cross-correlation peaks which appeared at the output correlation plane. Also, we employed the random-phase mask method to avoid displaying the usual second pair of correlation peaks at the output plane of the JTC architecture. The proposed JTC scheme was tested through a large number of simulations for low-resolution as well as high-resolution colored images. Both types of images were subjected to severe compression (up to a ratio of 94%) and strong densities of Gaussian and Salt & Peppers noises (up to 0.5). Further, noise-free and noisy-occluded reference images (up to 75%) are tested. We have demonstrated that low-resolution color images can afford large amounts of compression ratios and strong noise densities. The proposed scheme successfully detects the multiple compressed targets under all the above conditions.
The Oxygen Tank Lifter is designed to assist in the lifting and moving of compressed gas cylinders into and out of the storage compartment or lift onto high platforms. It helps you to move and lift cylinders into a vehicle or storage rack. The large 10-inch Foam Flat free rear wheels and stabilizing legs with front wheels provide a firm base when lifting a cylinder. A sure locking hold-down strap keeps the cylinder in place and the 12 volt winch provides precise lifting with a high strength nylon strap.
On every camera I shoot with, I always default to Lossless Compression, because it is the most efficient way to store RAW images. There is no benefit of shooting Uncompressed RAW and Lossy Compression results in loss of potentially valuable data, which I might need to recover shadow / highlight details in images. 2ff7e9595c
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