We describe a solution for image restoration in a computational
camera known as an extended depth of field
(EDOF) system. The specially-designed optics produce
point spread functions that are roughly invariant with object distance
in a range. However, this invariance involves a trade-off
with the peak sharpness of the lens. The lens blur
is a function of lens field-height, and the imaging sensor introduces signal-dependent noise. In this context, the principal contributions
of this paper are: a) the modeling of the EDOF focus recovery
problem; and b) the adaptive EDOF focus recovery approach, operating in signal-dependent noise.
The focus recovery solution is adaptive to complexities of an EDOF imaging system,
and performs a joint deblurring and noise
suppression. It also adapts to imaging conditions by accounting for the state of the sensor (e.g., low-light conditions).
Transform coding plays a central role in image and video coding
technologies. Various transforms, whether fixed or adaptive, have
been utilized for video compression. However, these transforms are
inherently sub-optimal in a coding-efficiency sense, given that
their design does not explicitly take into account the entire
transform coding model. Thus, commonly used transforms such as the
DCT have desirable properties, but were designed without the full
consideration of the other transform coding elements including
quantization and entropy coding. Although these transforms have
good performance in a rate-distortion sense, as demonstrated by
today's image and video coding standards, we show that superior
transforms can be designed based on the complete transform coding
model. We present a new class of transforms called coding-adaptive
transforms, that are derived under an optimality criterion which
incorporates the elements of transform coding and is formulated
for coding efficiency. Additionally, characteristics of the new
transforms such as adaptivity, generalization, and robustness are
discussed.
In this paper, we present topics related to tracking of video objects in compressed video databases, within the context of video retrieval applications. We developed a video retrieval and tracking system (VORTEX), to enable operation directly on compressed video data. The structure of the video compression standards is exploited in order to avoid the costly decompression operation. This is achieved by utilizing motion compensation - a critical prediction filter embedded in video compression standards - to eliminate and interpolate the desired method for template matching. Occlusion analysis, filtering and motion analysis are used to implement fast tracking of objects of interest on the compressed video data. Being presented with a query in the form of template images of objects, the system operates on the compressed video in order to find the images or video sequences, where those objects are present and their positions are in the image. This enables the retrieval and display of the query-relevant sequences.
In this paper, a novel visual search engine for video retrieval and tracking from compressed multimedia databases is proposed. Our approach exploits the structure of video compression standards in order to perform object matching directly on the compressed video data. This is achieved by utilizing motion compensation--a critical prediction filter embedded in video compression standards--to estimate and interpolate the desired method for template matching. Motion analysis is used to implement fast tracking of objects of interest on the compressed video data. Being presented with a query in the form of template images of objects, the system operates on the compressed video in order to find the images or video sequences where those objects are presented and their positions in the image. This in turn enables the retrieval and display of the query-relevant sequences.
Conference Committee Involvement (7)
Applications of Digital Image Processing XXXVII
18 August 2014 | San Diego, California, United States
Applications of Digital Image Processing XXXVI
26 August 2013 | San Diego, California, United States
Applications of Digital Image Processing XXXV
13 August 2012 | San Diego, California, United States
Applications of Digital Image Processing XXXIV
22 August 2011 | San Diego, California, United States
Visual Information Processing and Communication II
25 January 2011 | San Francisco Airport, California, United States
Applications of Digital Image Processing XXXIII
2 August 2010 | San Diego, California, United States
Visual Information Processing and Communication
19 January 2010 | San Jose, California, United States
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