Abstract:
Architecture for enhancing the compression (e.g., luma, chroma) of a video signal and improving the perceptual quality of the video compression schemes. The architecture operates to reshape the normal multimodal energy distribution of the input video signal to a new energy distribution. In the context of luma, the algorithm maps the black and white (or contrast) information of a picture to a new energy distribution. For example, the contrast can be enhanced in the middle range of the luma spectrum, thereby improving the contrast between a light foreground object and a dark background. At the same time, the algorithm reduces the bit-rate requirements at a particular quantization step size. The algorithm can be utilized also in post-processing to improve the quality of decoded video.

Description:
BACKGROUND 
     The Internet and other communications networks have evolved to facilitate not only the distribution of video media as files but also streaming video for use in business and consumer applications. For example, employees are now able to participate in business meetings while on travel via video conferencing. Additionally, the consumer can interact with family and friends via video communications using home computers and other capable devices. 
     However, realtime video communication can impose huge demands on the computing system and the network, thereby requiring video compression schemes that operate at very low bit rates while still achieving good perceptual quality. The need for good perceptual quality can be especially important for image processing in medical applications. For example, contrast enhancement in radiography and mammography is of particular importance in the medical field. Given that image processing is closely related to video processing, it is desirable to obtain compression techniques that provide at least quality image and video output and low impact on the associated communication aspects. 
     SUMMARY 
     The following presents a simplified summary in order to provide a basic understanding of some novel embodiments described herein. This summary is not an extensive overview, and it is not intended to identify key/critical elements or to delineate the scope thereof. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later. 
     The disclosed architecture includes an algorithm (or mapping function) that enhances the compression (e.g., luma, chroma) of a video signal, and thus, improves the perceptual quality of the video compression schemes. The architecture operates to reshape the normal multimodal energy distribution of the input video signal to a new energy distribution. 
     In the context of luma, the algorithm maps the black and white (or contrast) information of a picture to a new energy distribution (e.g., multimodal). In one implementation, the contrast is enhanced in the middle range of the luma spectrum, thereby improving the contrast between a light foreground object and a dark background. At the same time, the algorithm reduces the bit-rate requirements at a particular quantization step size. The algorithm can be utilized also in post-processing to improve the quality of decoded video. 
     The architecture does not require any change to the bit stream syntax or decoder, and hence, is backward compatible with existing video compression standards such as VC-1, H.263 and H.264, thereby improving the quality of video compression standards. The architecture can be used to improve the quality of all video compression algorithms used for realtime video communication (e.g., broadcasting) and/or non-realtime video applications (e.g., media archiving) 
     To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative, however, of but a few of the various ways in which the principles disclosed herein can be employed and is intended to include all such aspects and equivalents. Other advantages and novel features will become apparent from the following detailed description when considered in conjunction with the drawings. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates a computer-implemented picture processing system. 
         FIG. 2  illustrates a system that employs energy distribution remapping for the contrast (or luma (Y)) vector in video signal processing. 
         FIG. 3  illustrates a luma distribution graph showing remapping of a multimodal energy distribution to a new energy distribution. 
         FIG. 4  illustrates a graph that the remapping of luma distribution of  FIG. 3  reduces the bit rate for encoding purposes thereby retaining quality in the output signal during the encoding process. 
         FIG. 5  illustrates the remapping of energy distributions for luma (Y) and chroma (CbCr) in a video codec. 
         FIG. 6  illustrates the use of a look-up table for the remapping of multimodal energy distributions to more concentrated energy distributions. 
         FIG. 7  illustrates a system for the selective processing of pictures of an input video stream for contrast enhancement. 
         FIG. 8  illustrates a method of processing pictures. 
         FIG. 9  illustrates a method of processing contrast in pictures. 
         FIG. 10  illustrates a method of computing a mapping function. 
         FIG. 11  illustrates a block diagram of a computing system operable to execute energy distribution remapping in accordance with the disclosed architecture. 
     
    
    
     DETAILED DESCRIPTION 
     The disclosed architecture is novel energy redistribution technique that can be applied to luma and chroma space to redistribute multimodal energy in video signals in realtime pre- and/or post-processing functions. The remapping or redistribution function improves the perceived image quality by enhancing the contrast, for example, in the middle range of the luma spectrum. The technique improves the contrast between a light foreground object and a dark background, for example. The technique also removes multimodal regions of the luma signal within the bright or dark regions of the spectrum, and can be used to improve the quality of all video compression algorithms used for realtime video communication (e.g., broadcasting) and/or non-realtime video applications (e.g., media archiving) 
     Reference is now made to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding thereof. It may be evident, however, that the novel embodiments can be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate a description thereof. 
       FIG. 1  illustrates a computer-implemented picture processing system  100 . The system  100  includes an input component  102  for receiving a video signal associated with a picture  104 , the picture  104  including a multimodal energy distribution. The system  100  can also include a mapping component  106  (e.g., function or look-up table (LUT)) for mapping the multimodal energy distribution of the picture to a new energy distribution to improve quality in the picture. 
     The term picture, as used throughout this description is intended to include a frame or a field. A frame is an image captured at some point in time. A field includes the set of lines (or every other line) that form an image at some point in time. Interlaced video is more often associated with the coding of pictures as fields. 
     This technique does not require any change to the bit stream syntax or decoder, and is compatible with video compression standards such as VC-1, H.263 and H.264 (also referred to as MPEG-4, and which uses slices rather than picture designations), for example. Hence, the technique can be used to improve the quality of video compression standards. 
       FIG. 2  illustrates a system  200  that employs energy distribution remapping for the contrast (or luma (Y)) vector in video signal processing. The system  200  includes a video codec  202  (e.g., lossless or lossy) that receives a video-in signal  204  (e.g., analog or digital) for encoding by a luma encoder  206  and decoding by a luma decoder  208  to create a video-out signal  210 . Here, input processing in the encoder  206  includes utilization of the input component  102  and the mapping component  106  for remapping the multimodal energy distribution for contrast to a new energy distribution (e.g., single mode). The remapped signal is then passed to the decoder  208  for decoding as the video-out signal  210 . 
     Note that although shown only as part of the encoder  206 , post-processing in the decoder  208  can employ the input component  102  and mapping component  106  in combination with these components ( 102  and  106 ) in the encoder  206 , or alternatively, to use in the encoder  206 . In this case, only the perceptual quality is improved, and there is no change in bit rate. 
       FIG. 3  illustrates a luma distribution graph  300  showing remapping of a multimodal energy distribution  302  to a new energy distribution  304 . In other words, the remapping by the mapping component reduces the number of peaks (or more concentrated) in the multimodal representation relative to the new distribution  304 . Here, the redistribution is approximate to the middle range of the multimodal energy distribution  302 . However, this is not to be construed as a limitation, in that the redistribution can be in any general area along the spectrum, from the lower end to the upper end. For example, redistribution can result in a new more concentrated lower range energy distribution  306  or a new more concentrated upper range energy distribution  308 . No stretching or offsetting is required. Moreover, this technique is not restricted to contrast, but can also be employed for chroma signals. 
       FIG. 4  illustrates a graph  400  that the remapping of luma distribution of  FIG. 3  reduces the bit rate for encoding purposes thereby retaining quality in the output signal during the encoding process. The motivation is to reduce the overall signal amplitude along the vertical axis (the number of appearances). The input signal  402  is reduced to the redistributed signal  404  making the probability (Y) more concentrated rather than multimodal. The area under the input signal  402  is exactly the area under the redistributed signal  404 . 
       FIG. 5  illustrates the remapping of energy distributions for luma (Y) and chroma (CbCr) in a video codec  500 . The color portion of video can also obtain the benefits associated with the remapping of energy distributions. The codec  500  illustrates the utilization of remapping for luma and chroma; however, it is to be understood that the codec  500  can include remapping only for the color signals, only the luma signals (as in  FIG. 2 ), or the combination of both luma and color. 
     Here, the codec  500  is illustrated with three separate codec sections for video signal processing: a luma section  502 , a chroma Cb section  504 , and a chroma Cr section  506 . However, it is to be appreciated that a single codec can be utilized for all three sections ( 502 ,  504  and  506 ). Moreover, as previously indicated, it is not a requirement that remapping be employed in post-processing as well as pre-processing. Thus, the decoders do not need to include remapping functionality. Additionally, not shown is a video interface for splitting out the luma and chroma signals from the video-in signal  204  to the separate encoder or a video interface for combining the remapping signals into the video-out signal  210 . 
     The luma encoder  206  is shown as including a pre-processing input component  502  and mapping component  504 , and the luma decoder  506  includes a post-processing input component  508  and mapping component  510 . Similarly, a chroma Cb encoder  512  is shown as including a pre-processing input component  514  and a mapping component  516 , and the chroma Cb decoder  518  includes a post-processing input component  520  and a mapping component  522 . A chroma Cr encoder  524  is shown as including a pre-processing input component  526  and a mapping component  528 , and the chroma Cr decoder  530  includes a post-processing input component  532  and a mapping component  534 . 
     Variations on this system  500  can include a single encoder that receives or generates the split-out luma and chroma signals using a single mapping component or LUT for operating on the signals, separate encoders for the three inputs and a single decoder, a single luma encoder and a single chroma encoder (for both Cb and Cr) both using separate decoders or the same decoder, and so on. 
       FIG. 6  illustrates the use of a LUT  602  for the remapping of multimodal energy distributions to more concentrated energy distributions. The video codec  202  comprises the encoder  206 , shown as including the input component  102  for receiving the video-in signal  204  (via a video interface (not shown)) and the LUT  602 , and the decoder  208  is unchanged. The remapped signal using the LUT  602  is passed to the decoder  208  for generation of the video-out signal  210  (e.g., via a video interface (not shown)). The LUT  602  can be programmable (e.g., during blanking intervals). The LUT  602  can be updated in realtime by a graphics processor where the LUT  602  can be stored in graphics adapter memory (e.g., dual-port RAM, flash, etc.). In another embodiment, the LUT  602  can be used for the encoder  208  while the remapping component (not shown) can be used in the decoder  208 , for example, or vice versa. 
       FIG. 7  illustrates a system  700  for the selective processing of pictures of an input video stream for contrast enhancement. The remapping process can be performed for every picture of a set of input pictures  702  of a video stream which is more compute intensive than selecting less than all pictures for processing. For example, it may be more desirable to select every other picture, or every third picture. In order to make more intelligent decisions, the system  700  includes an encoder  704  that includes not only the input component  102  and mapping component  106 , but also a selection component  706  for selecting the pictures to process and a cost component  708  for computing a cost associated with selection of the number of input pictures. For example, the cost may be high to process all of the input pictures  704 , whereas the cost can be less when selecting a subset of the input pictures  704 . The cost component  708  can include, for example, a statistical or probabilistic algorithm for computing cost of tradeoffs in processing all pictures or a subset of the pictures. The pictures  704  are received by a video interface  710  that splits out the signal components into luma and chroma signals, for example. 
     The cost can be based on a number of factors such as quality of the output (as a feedback parameter), complexity (e.g., color content) of the pictures (black and white versus color), processing capabilities of the hardware/software system employed, dynamic change (e.g., contrast) from picture to picture, and so on. The selection can be preconfigured such as every other picture, every third picture, etc., and/or set for dynamic operation where the encoder dynamically selects from all pictures to a subset of the pictures for energy distribution remapping in realtime. The cost component  708  provides a mechanism for computing a tradeoff between performance and complexity. 
     The remapping function can be computed for the I-pictures (intra-pictures), and the same remapping function is then applied for all subsequent P-pictures (predicted) and B-pictures (bi-predicted) in a group of pictures (GOP). The use of same preprocessing mapping component  106  (e.g., a function or a LUT) for the GOP eliminates temporal flickering within the GOP, and also minimizes the bit rate of P- and B-pictures. The computational complexity required to compute the remapping function is also reduced, since the function is computed only once for every GOP. To reduce complexity, the actual remapping can be implemented as the LUT. This applies to slices or I-frames or key frames as well. 
     Following is a series of flow charts representative of exemplary methodologies for performing novel aspects of the disclosed architecture. While, for purposes of simplicity of explanation, the one or more methodologies shown herein, for example, in the form of a flow chart or flow diagram, are shown and described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology could alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all acts illustrated in a methodology may be required for a novel implementation. 
       FIG. 8  illustrates a method of processing pictures. At  800 , energy distributions of pictures in a video signal are measured. At  802 , the energy distributions of the pictures are mapped into new concentrated energy distributions along a signal spectrum to enhance quality in the pictures. The mapping process removes multimodal regions in the energy distributions when mapping the energy distributions to the new concentrated energy distributions. The mapping of a new concentrated energy distribution can be in a mid-range of a luma signal spectrum to control contrast of a picture. The mapping of a new concentrated energy distribution can be in a chroma spectrum of a picture. The method can further comprise generating a baseline value for a darkest portion of a picture and a baseline value for a brightest portion of the picture. The method can further comprise computing a mapping function for an I-Frame and applying the mapping function for P-Frames and B-Frames in a group of the pictures. 
       FIG. 9  illustrates a method of processing contrast in pictures. At  900 , energy distributions of the pictures of a video signal are measured in luma space. At  902 , the energy distributions are then mapped into new energy distributions in luma space to enhance contrast quality in the pictures. The method can also include removing multimodal effects in the energy distributions when mapping to the new energy distributions. The new energy distributions (having the multimodal effects removed) are mapped to the mid-range of the luma space. As part of measuring the energy distributions, the baseline value for a darkest portion of a picture in the luma space is computed as well as and a baseline value for a brightest portion of the picture in the luma space. The mapping process can be based on a mapping function computed for mapping the energy distributions into the new energy distributions. The mapping function can be computed for an I-Frame and applied to P-Frames and B-Frames in a group of the pictures. Alternatively, or in combination therewith, the mapping process can employ a look-up table via which the mapping of the energy distributions is performed. 
       FIG. 10  illustrates a method of computing a mapping function. At  1000 , the energy distribution is measured in luma space. At  1002 , the darkest portion of the image is computed as (x1%) or the darkest (x2%) in the whole image. At  1004 , the brightest portion of the image is computed as (y1%) or the brightest (y2%) in the whole image. At  1006 , the energy is redistributed by maintaining the darkest portion cut point (or baseline) as either x1 or x2 mark, depending on the distribution. At  1008 , the energy is redistributed by also maintaining the brightest portion (or baseline) as either y1 or y2 mark, depending on the distribution. At  1010 , the rest of the energy is then adjusted to enhance the mid-range in the luma spectrum. 
     The mapping function compacts the energy distribution in the less important dark and light regions on the video. The compaction reduces the bit-rate that is required to code these regions. The contrast enhancement in the mid-range of the luma spectrum improves the perceptual quality as it increases the contrast between foreground and background areas. 
     As used in this application, the terms “component” and “system” are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component can be, but is not limited to being, a process running on a processor, a processor, a hard disk drive, multiple storage drives (of optical and/or magnetic storage medium), an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components can reside within a process and/or thread of execution, and a component can be localized on one computer and/or distributed between two or more computers. 
     Referring now to  FIG. 11 , there is illustrated a block diagram of a computing system  1100  operable to execute energy distribution remapping in accordance with the disclosed architecture. In order to provide additional context for various aspects thereof,  FIG. 11  and the following discussion are intended to provide a brief, general description of a suitable computing system  1100  in which the various aspects can be implemented. While the description above is in the general context of computer-executable instructions that may run on one or more computers, those skilled in the art will recognize that a novel embodiment also can be implemented in combination with other program modules and/or as a combination of hardware and software. 
     Generally, program modules include routines, programs, components, data structures, etc., that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the inventive methods can be practiced with other computer system configurations, including single-processor or multiprocessor computer systems, minicomputers, mainframe computers, as well as personal computers, hand-held computing devices, microprocessor-based or programmable consumer electronics, and the like, each of which can be operatively coupled to one or more associated devices. 
     The illustrated aspects can also be practiced in distributed computing environments where certain tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in both local and remote memory storage devices. 
     A computer typically includes a variety of computer-readable media. Computer-readable media can be any available media that can be accessed by the computer and includes volatile and non-volatile media, removable and non-removable media. By way of example, and not limitation, computer-readable media can comprise computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital video disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer. 
     With reference again to  FIG. 11 , the exemplary computing system  1100  for implementing various aspects includes a computer  1102  having a processing unit  1104 , a system memory  1106  and a system bus  1108 . The system bus  1108  provides an interface for system components including, but not limited to, the system memory  1106  to the processing unit  1104 . The processing unit  1104  can be any of various commercially available processors. Dual microprocessors and other multi-processor architectures may also be employed as the processing unit  1104 . 
     The system bus  1108  can be any of several types of bus structure that may further interconnect to a memory bus (with or without a memory controller), a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory  1106  can include non-volatile memory (NON-VOL)  1110  and/or volatile memory  1112  (e.g., random access memory (RAM)). A basic input/output system (BIOS) can be stored in the non-volatile memory  1110  (e.g., ROM, EPROM, EEPROM, etc.), which BIOS are the basic routines that help to transfer information between elements within the computer  1102 , such as during start-up. The volatile memory  1112  can also include a high-speed RAM such as static RAM for caching data. 
     The computer  1102  further includes an internal hard disk drive (HDD)  1114  (e.g., EIDE, SATA), which internal HDD  1114  may also be configured for external use in a suitable chassis, a magnetic floppy disk drive (FDD)  1116 , (e.g., to read from or write to a removable diskette  1118 ) and an optical disk drive  1120 , (e.g., reading a CD-ROM disk  1122  or, to read from or write to other high capacity optical media such as a DVD). The HDD  1114 , FDD  1116  and optical disk drive  1120  can be connected to the system bus  1108  by a HDD interface  1124 , an FDD interface  1126  and an optical drive interface  1128 , respectively. The HDD interface  1124  for external drive implementations can include at least one or both of Universal Serial Bus (USB) and IEEE 1394 interface technologies. 
     The drives and associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, and so forth. For the computer  1102 , the drives and media accommodate the storage of any data in a suitable digital format. Although the description of computer-readable media above refers to a HDD, a removable magnetic diskette (e.g., FDD), and a removable optical media such as a CD or DVD, it should be appreciated by those skilled in the art that other types of media which are readable by a computer, such as zip drives, magnetic cassettes, flash memory cards, cartridges, and the like, may also be used in the exemplary operating environment, and further, that any such media may contain computer-executable instructions for performing novel methods of the disclosed architecture. 
     A number of program modules can be stored in the drives and volatile memory  1112 , including an operating system  1130 , one or more application programs  1132 , other program modules  1134 , and program data  1136 . All or portions of the operating system, applications, modules, and/or data can also be cached in the volatile memory  1112 . It is to be appreciated that the disclosed architecture can be implemented with various commercially available operating systems or combinations of operating systems. 
     A user can enter commands and information into the computer  1102  through one or more wire/wireless input devices, for example, a keyboard  1138  and a pointing device, such as a mouse  1140 . Other input devices (not shown) may include a microphone, an IR remote control, a joystick, a game pad, a stylus pen, touch screen, or the like. These and other input devices are often connected to the processing unit  1104  through an input device interface  1142  that is coupled to the system bus  1108 , but can be connected by other interfaces such as a parallel port, IEEE 1394 serial port, a game port, a USB port, an IR interface, etc. 
     A monitor  1144  or other type of display device is also connected to the system bus  1108  via an interface, such as a video adaptor  1146 . The video adaptor  1146  can include the hardware and/or software in the form of input component  102 , mapping component  106 , the video codec  202  and associated components (e.g., mapping function or LUT  602 ), the video codec  500  and associated components, and encoder  704  and associated selection component  706  and cost component  708 , for example. 
     In addition to the monitor  1144 , a computer typically includes other peripheral output devices (not shown), such as speakers, printers, etc. 
     The computer  1102  may operate in a networked environment using logical connections via wire and/or wireless communications to one or more remote computers, such as a remote computer(s)  1148 . The remote computer(s)  1148  can be a workstation, a server computer, a router, a personal computer, portable computer, microprocessor-based entertainment appliance, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer  1102 , although, for purposes of brevity, only a memory/storage device  1150  is illustrated. The logical connections depicted include wire/wireless connectivity to a local area network (LAN)  1152  and/or larger networks, for example, a wide area network (WAN)  1154 . Such LAN and WAN networking environments are commonplace in offices and companies, and facilitate enterprise-wide computer networks, such as intranets, all of which may connect to a global communications network, for example, the Internet. 
     When used in a LAN networking environment, the computer  1102  is connected to the LAN  1152  through a wire and/or wireless communication network interface or adaptor  1156 . The adaptor  1156  can facilitate wire and/or wireless communications to the LAN  1152 , which may also include a wireless access point disposed thereon for communicating with the wireless functionality of the adaptor  1156 . 
     When used in a WAN networking environment, the computer  1102  can include a modem  1158 , or is connected to a communications server on the WAN  1154 , or has other means for establishing communications over the WAN  1154 , such as by way of the Internet. The modem  1158 , which can be internal or external and a wire and/or wireless device, is connected to the system bus  1108  via the input device interface  1142 . In a networked environment, program modules depicted relative to the computer  1102 , or portions thereof, can be stored in the remote memory/storage device  1150 . It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers can be used. 
     The computer  1102  is operable to communicate with wire and wireless devices or entities using the IEEE 802 family of standards, such as wireless devices operatively disposed in wireless communication (e.g., IEEE 802.11 over-the-air modulation techniques) with, for example, a printer, scanner, desktop and/or portable computer, personal digital assistant (PDA), communications satellite, any piece of equipment or location associated with a wirelessly detectable tag (e.g., a kiosk, news stand, restroom), and telephone. This includes at least Wi-Fi (or Wireless Fidelity), WiMax, and Bluetooth™ wireless technologies. Thus, the communication can be a predefined structure as with a conventional network or simply an ad hoc communication between at least two devices. Wi-Fi networks use radio technologies called IEEE 802.11x (a, b, g, etc.) to provide secure, reliable, fast wireless connectivity. A Wi-Fi network can be used to connect computers to each other, to the Internet, and to wire networks (which use IEEE 802.3-related media and functions). 
     What has been described above includes examples of the disclosed architecture. It is, of course, not possible to describe every conceivable combination of components and/or methodologies, but one of ordinary skill in the art may recognize that many further combinations and permutations are possible. Accordingly, the novel architecture is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term “includes” is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term “comprising” as “comprising” is interpreted when employed as a transitional word in a claim.